Repository: Andrew-Qibin/DSS
Branch: master
Commit: 5dc7e1ceb768
Files: 12
Total size: 1.2 MB

Directory structure:
gitextract_bm0anyem/

├── DSS-tutorial.ipynb
├── LICENSE
├── README.md
├── deploy.prototxt
├── lists/
│   └── train.lst
├── resnet/
│   ├── Readme.md
│   ├── deploy_resnet.prototxt
│   ├── sal_data_layer.py
│   └── train_resnet.prototxt
├── run_saliency.py
├── solver.prototxt
└── train_val.prototxt

================================================
FILE CONTENTS
================================================

================================================
FILE: DSS-tutorial.ipynb
================================================
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   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "import scipy.misc\n",
    "import Image\n",
    "import scipy.io\n",
    "import os\n",
    "\n",
    "\n",
    "# Make sure that caffe is on the python path:\n",
    "caffe_root = '../../../'\n",
    "import sys\n",
    "sys.path.insert(0, caffe_root + 'python')\n",
    "\n",
    "import caffe\n",
    "\n",
    "EPSILON = 1e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_root = '/opt/dataset/saliency/msra/Imgs/'\n",
    "with open('../../../data/msra_b/test.lst') as f:\n",
    "    test_lst = f.readlines()\n",
    "    \n",
    "test_lst = [data_root+x.strip() for x in test_lst]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "#remove the following two lines if testing with cpu\n",
    "caffe.set_mode_gpu()\n",
    "# choose which GPU you want to use\n",
    "caffe.set_device(0)\n",
    "caffe.SGDSolver.display = 0\n",
    "# load net\n",
    "net = caffe.Net('deploy.prototxt', 'dss_model_released.caffemodel', caffe.TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Visualization\n",
    "def plot_single_scale(scale_lst, name_lst, size):\n",
    "    pylab.rcParams['figure.figsize'] = size, size/2\n",
    "    plt.figure()\n",
    "    for i in range(0, len(scale_lst)):\n",
    "        s = plt.subplot(1,5,i+1)\n",
    "        s.set_xlabel(name_lst[i], fontsize=10)\n",
    "        if name_lst[i] == 'Source':\n",
    "            plt.imshow(scale_lst[i])\n",
    "        else:\n",
    "            plt.imshow(scale_lst[i], cmap = cm.Greys_r)\n",
    "        s.set_xticklabels([])\n",
    "        s.set_yticklabels([])\n",
    "        s.yaxis.set_ticks_position('none')\n",
    "        s.xaxis.set_ticks_position('none')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "idx = 39\n",
    "\n",
    "# load image\n",
    "img = Image.open(test_lst[idx])\n",
    "img = np.array(img, dtype=np.uint8)\n",
    "im = np.array(img, dtype=np.float32)\n",
    "im = im[:,:,::-1]\n",
    "im -= np.array((104.00698793,116.66876762,122.67891434))\n",
    "im = im.transpose((2,0,1))\n",
    "\n",
    "# load gt\n",
    "gt = Image.open(test_lst[idx][:-4] + '.png')\n",
    "\n",
    "# shape for input (data blob is N x C x H x W), set data\n",
    "net.blobs['data'].reshape(1, *im.shape)\n",
    "net.blobs['data'].data[...] = im\n",
    "# run net and take argmax for prediction\n",
    "net.forward()\n",
    "out1 = net.blobs['sigmoid-dsn1'].data[0][0,:,:]\n",
    "out2 = net.blobs['sigmoid-dsn2'].data[0][0,:,:]\n",
    "out3 = net.blobs['sigmoid-dsn3'].data[0][0,:,:]\n",
    "out4 = net.blobs['sigmoid-dsn4'].data[0][0,:,:]\n",
    "out5 = net.blobs['sigmoid-dsn5'].data[0][0,:,:]\n",
    "out6 = net.blobs['sigmoid-dsn6'].data[0][0,:,:]\n",
    "fuse = net.blobs['sigmoid-fuse'].data[0][0,:,:]\n",
    "res = (out3 + out4 + out5 + fuse) / 4\n",
    "res = (res - np.min(res) + EPSILON) / (np.max(res) - np.min(res) + EPSILON)\n",
    "out_lst = [out1, out2, out3, out4, out5]\n",
    "name_lst = ['SO1', 'SO2', 'SO3', 'SO4', 'SO5']\n",
    "plot_single_scale(out_lst, name_lst, 10)\n",
    "out_lst = [out6, fuse, res, img, gt]\n",
    "name_lst = ['SO6', 'Fuse', 'Result', 'Source', 'GT']\n",
    "plot_single_scale(out_lst, name_lst, 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2019 Qibin Hou (andrewhoux@gmail.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
## Deeply Supervised Salient Object Detection with Short Connections

## What's new!!!

A new fast approach is now available. [Here](https://github.com/Andrew-Qibin/PoolNet) is the PyTorch implementation.
[Here](https://mmcheng.net/poolnet/) is the link to the project page.

### Network architecture and more details
Please refer to [our paper](https://arxiv.org/abs/1611.04849).

### Usage
Please install [Caffe](https://github.com/BVLC/caffe) first. I think you may find a great number of tutorials talking about how to install it.
```bash
cd <caffe_root>/examples
git clone https://github.com/Andrew-Qibin/DSS.git
```
Before you start, you also need our pretrained model.
```bash
wget http://mftp.mmcheng.net/Andrew/dss_model_released.caffemodel
```
You can also download it from [here (google drive)](https://drive.google.com/file/d/0B21WWgRw0U1uMFVKN3NPT0VHMW8/view?usp=sharing). The results produced by this model are slightly different from the ones we reported in our paper (with higher F-measure score and also higher MAE score).


If you want to train the model, please prepare your own training dataset first. The data layer we used here is similar to the one used in [HED](https://github.com/s9xie/hed). You can also refer to the data layer used in [Deeplab](https://bitbucket.org/aquariusjay/deeplab-public-ver2) or write your own one. 

You may also find our data layer [here](https://github.com/Andrew-Qibin/caffe_dss). Notice that if you use caffe, please cite their paper.

Then, run
```bash
python run_saliency.py
```

If you want to test the model, you can run
```bash
ipython notebook DSS-tutorial.ipynb
```  

About the CRF code we used, you can find it [here](https://github.com/Andrew-Qibin/dss_crf). Notice that please provide a link to the original code as a footnote or a citation if you plan to use it.

### Visual comparison with previous start-of-the-arts
![](https://github.com/Andrew-Qibin/DSS/blob/master/Compares.png)
From left to right: Source, Groundtruth, Ours, DCL, DHS, RFCN, DS, MDF, ELD, MC, DRFI, DSR.

### Useful links that you might want
* [MSRAB](https://people.cs.umass.edu/~hzjiang/drfi/index.html): including 2500 training, 500 validation, and 2000 test images. (This is also our training set.) The source images can be found [here](http://mmcheng.net/msra10k/).
* [MSRA10K](http://mmcheng.net/msra10k/): You can also use this dataset for training as some works did.
* [Evaluation Code (Windows)](https://github.com/MingMingCheng/CmCode/tree/master/CmLib/Illustration): The cold is based on MS Visual Studio.
* [Evaluation Code (Ubuntu)](https://github.com/Andrew-Qibin/SalMetric): This code is based on C++ and with a python wrapper for python users.

We add the resnet version of our model into this repo. Also, a larger set of training data can be found in the
lists dir. ResNet version caffemodel can be found [here (google drive)](https://drive.google.com/open?id=1psapbqiKkVQQNL_uUC9Mv2Hfio5m0TKS).

### If you want to compare your results with ours, you may download them from here [(Baidu Drive)](https://pan.baidu.com/s/1o8r90Ga) or [(Google Drive)](https://drive.google.com/open?id=1ppil6z-JzjMoSd4faZgAjTWT_Zya4td5).

### If you think this work is helpful, please cite
```latex
@article{HouPami19Dss,
  title={Deeply Supervised Salient Object Detection with Short Connections},
  author={Hou, Qibin and Cheng, Ming-Ming and Hu, Xiaowei and Borji, Ali and Tu, Zhuowen and Torr, Philip},
  year  = {2019},
  volume={41}, 
  number={4}, 
  pages={815-828}, 
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}
}

```


================================================
FILE: deploy.prototxt
================================================
### DSS ###
name: "DSS"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 500
input_dim: 500

layer { bottom: 'data' top: 'conv1_1' name: 'conv1_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 5 kernel_size: 3 } }
layer { bottom: 'conv1_1' top: 'conv1_1' name: 'relu1_1' type: "ReLU" }
layer { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: "ReLU" }
layer { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: "ReLU" }
layer { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: "ReLU" }
layer { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: "ReLU" }
layer { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: "ReLU" }
layer { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: "ReLU" }
layer { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: "ReLU" }
layer { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: "ReLU" }
layer { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: "ReLU" }
layer { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } }

layer { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: "ReLU" }
layer { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: "ReLU" }
layer { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: "ReLU" }
layer { bottom: 'conv5_3' top: 'pool5' name: 'pool5' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } }
layer { bottom: 'pool5' top: 'pool5a' name: 'pool5a' type: "Pooling"
  pooling_param { pool: AVE kernel_size: 3 stride: 1 pad: 1 } }

###DSN conv 6###
layer { name: 'conv1-dsn6' type: "Convolution" bottom: 'pool5a' top: 'conv1-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn6' top: 'conv1-dsn6' name: 'relu1-dsn6' type: "ReLU" }
layer { name: 'conv2-dsn6' type: "Convolution" bottom: 'conv1-dsn6' top: 'conv2-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn6' top: 'conv2-dsn6' name: 'relu2-dsn6' type: "ReLU" }
layer { name: 'conv3-dsn6' type: "Convolution" bottom: 'conv2-dsn6' top: 'conv3-dsn6'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_in_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up' bottom: 'data' top: 'upscore-dsn6' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn6" bottom: "label" top: "loss-dsn6"
  name: "loss-dsn6" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn6" bottom: "upscore-dsn6" top: "sigmoid-dsn6" include { phase: TEST} }

###DSN conv 5###
layer { name: 'conv1-dsn5' type: "Convolution" bottom: 'conv5_3' top: 'conv1-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn5' top: 'conv1-dsn5' name: 'relu1-dsn5' type: "ReLU" }
layer { name: 'conv2-dsn5' type: "Convolution" bottom: 'conv1-dsn5' top: 'conv2-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn5' top: 'conv2-dsn5' name: 'relu2-dsn5' type: "ReLU" }
layer { name: 'conv3-dsn5' type: "Convolution" bottom: 'conv2-dsn5' top: 'conv3-dsn5'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_in_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top: "loss-dsn5"
  name: "loss-dsn5" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn5" bottom: "upscore-dsn5" top: "sigmoid-dsn5" include { phase: TEST} }

###DSN conv 4###
layer { name: 'conv1-dsn4' type: "Convolution" bottom: 'conv4_3' top: 'conv1-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn4' top: 'conv1-dsn4' name: 'relu1-dsn4' type: "ReLU" }
layer { name: 'conv2-dsn4' type: "Convolution" bottom: 'conv1-dsn4' top: 'conv2-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn4' top: 'conv2-dsn4' name: 'relu2-dsn4' type: "ReLU" }
layer { name: 'conv3-dsn4' type: "Convolution" bottom: 'conv2-dsn4' top: 'conv3-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn6-4' }
layer { type: "Deconvolution" name: 'upsample2_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn5-4' }
layer { name: "concat-dsn4" bottom: "conv3-dsn4"  bottom: "upscore-dsn6-4" bottom: "upscore-dsn5-4" 
  top: "concat-dsn4" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn4' type: "Convolution" bottom: 'concat-dsn4' top: 'conv4-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_in_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top: "loss-dsn4"
  name: "loss-dsn4" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn4" bottom: "upscore-dsn4" top: "sigmoid-dsn4" include { phase: TEST} }

### DSN conv 3 ###
layer { name: 'conv1-dsn3' type: "Convolution" bottom: 'conv3_3' top: 'conv1-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn3' top: 'conv1-dsn3' name: 'relu1-dsn3' type: "ReLU" }
layer { name: 'conv2-dsn3' type: "Convolution" bottom: 'conv1-dsn3' top: 'conv2-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn3' top: 'conv2-dsn3' name: 'relu2-dsn3' type: "ReLU" }
layer { name: 'conv3-dsn3' type: "Convolution" bottom: 'conv2-dsn3' top: 'conv3-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn6-3' }
layer { type: "Deconvolution" name: 'upsample4_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn5-3' }
layer { name: "concat-dsn3" bottom: "conv3-dsn3"  bottom: "upscore-dsn6-3" bottom: "upscore-dsn5-3" 
  top: "concat-dsn3" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn3' type: "Convolution" bottom: 'concat-dsn3' top: 'conv4-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_in_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top: "loss-dsn3"
  name: "loss-dsn3" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn3" bottom: "upscore-dsn3" top: "sigmoid-dsn3" include { phase: TEST} }

### DSN conv 2 ###
layer { name: 'conv1-dsn2' type: "Convolution" bottom: 'conv2_2' top: 'conv1-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn2' top: 'conv1-dsn2' name: 'relu1-dsn2' type: "ReLU" }
layer { name: 'conv2-dsn2' type: "Convolution" bottom: 'conv1-dsn2' top: 'conv2-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn2' top: 'conv2-dsn2' name: 'relu2-dsn2' type: "ReLU" }
layer { name: 'conv3-dsn2' type: "Convolution" bottom: 'conv2-dsn2' top: 'conv3-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn6-2' }
layer { type: "Deconvolution" name: 'upsample8_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn5-2' }
layer { type: "Deconvolution" name: 'upsample4_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn4-2' }
layer { type: "Deconvolution" name: 'upsample2_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn3-2' }
layer { name: "concat-dsn2" bottom: "conv3-dsn2" bottom: "upscore-dsn5-2" bottom: "upscore-dsn4-2" bottom: "upscore-dsn6-2"
  bottom: "upscore-dsn3-2" top: "concat-dsn2" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn2' type: "Convolution" bottom: 'concat-dsn2' top: 'conv4-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample2_in_dsn2' bottom: 'conv4-dsn2' top: 'score-dsn2-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top: "loss-dsn2"
  name: "loss-dsn2" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn2" bottom: "upscore-dsn2" top: "sigmoid-dsn2" include { phase: TEST} }

## DSN conv 1 ###
layer { name: 'conv1-dsn1' type: "Convolution" bottom: 'conv1_2' top: 'conv1-dsn1'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn1' top: 'conv1-dsn1' name: 'relu1-dsn1' type: "ReLU" }
layer { name: 'conv2-dsn1' type: "Convolution" bottom: 'conv1-dsn1' top: 'conv2-dsn1'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn1' top: 'conv2-dsn1' name: 'relu2-dsn1' type: "ReLU" }
layer { name: 'conv3-dsn1' type: "Convolution" bottom: 'conv2-dsn1' top: 'conv3-dsn1'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn6-1' }
layer { type: "Deconvolution" name: 'upsample16_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn5-1' }
layer { type: "Deconvolution" name: 'upsample8_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn4-1' }
layer { type: "Deconvolution" name: 'upsample4_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn3-1' }
layer { name: "concat-dsn1" bottom: "conv3-dsn1"  bottom: "upscore-dsn5-1" bottom: "upscore-dsn4-1" bottom: "upscore-dsn6-1"
  bottom: "upscore-dsn3-1" top: "concat-dsn1" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn1' type: "Convolution" bottom: 'concat-dsn1' top: 'score-dsn1-up'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn1-up' bottom: 'data' top: 'upscore-dsn1' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn1" bottom: "label" top: "loss-dsn1"
  name: "loss-dsn1" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn1" bottom: "upscore-dsn1" top: "sigmoid-dsn1" include { phase: TEST } }

### Concat and multiscale weight layer ###
layer { name: "concat" bottom: "upscore-dsn1"  bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" 
  bottom: "upscore-dsn5" bottom: "upscore-dsn6" top: "concat-upscore" type: "Concat" concat_param { concat_dim: 1 } }
layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.1667 } } }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top: "loss-fuse"
  name: "loss-fuse" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-fuse" bottom: "upscore-fuse" top: "sigmoid-fuse" include { phase: TEST } }


================================================
FILE: lists/train.lst
================================================
/opt/dataset/saliency/MSRA-B/Imgs/0_0_147.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_147.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_155.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_155.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_280.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_280.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_284.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_284.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_431.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_431.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_547.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_547.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_579.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_579.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_735.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_735.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_77.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_77.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_818.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_818.png
/opt/dataset/saliency/MSRA-B/Imgs/0_0_951.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_0_951.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10134.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10134.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10135.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10135.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10176.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10176.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10343.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10343.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10392.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10392.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10396.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10396.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10484.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10484.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10507.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10507.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10545.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10545.png
/opt/dataset/saliency/MSRA-B/Imgs/0_10_10938.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_10_10938.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11179.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11179.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11219.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11219.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11281.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11281.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11313.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11313.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11325.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11325.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11435.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11435.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11459.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11459.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11480.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11480.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11533.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11533.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11557.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11557.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11830.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11830.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11875.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11875.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11924.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11924.png
/opt/dataset/saliency/MSRA-B/Imgs/0_11_11987.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_11_11987.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12000.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12000.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12048.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12048.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12072.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12072.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12322.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12322.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12372.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12372.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12429.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12429.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12597.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12597.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12693.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12693.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12705.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12705.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12750.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12750.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12771.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12771.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12816.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12816.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12833.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12833.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12884.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12884.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12921.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12921.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12923.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12923.png
/opt/dataset/saliency/MSRA-B/Imgs/0_12_12974.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_12_12974.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13036.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13036.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13264.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13264.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13308.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13308.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13339.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13339.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13349.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13349.png
/opt/dataset/saliency/MSRA-B/Imgs/0_13_13356.jpg /opt/dataset/saliency/MSRA-B/Imgs/0_13_13356.png
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/opt/dataset/saliency/HKU-IS-full/Imgs/0385.png /opt/dataset/saliency/HKU-IS-full/gt/0385.png
/opt/dataset/saliency/HKU-IS-full/Imgs/0010.png /opt/dataset/saliency/HKU-IS-full/gt/0010.png
/opt/dataset/saliency/HKU-IS-full/Imgs/5475.png /opt/dataset/saliency/HKU-IS-full/gt/5475.png
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/opt/dataset/saliency/HKU-IS-full/Imgs/0100.png /opt/dataset/saliency/HKU-IS-full/gt/0100.png
/opt/dataset/saliency/HKU-IS-full/Imgs/6154.png /opt/dataset/saliency/HKU-IS-full/gt/6154.png
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/opt/dataset/saliency/HKU-IS-full/Imgs/6065.png /opt/dataset/saliency/HKU-IS-full/gt/6065.png
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/opt/dataset/saliency/HKU-IS-full/Imgs/6610.png /opt/dataset/saliency/HKU-IS-full/gt/6610.png
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================================================
FILE: resnet/Readme.md
================================================
aa


================================================
FILE: resnet/deploy_resnet.prototxt
================================================
### DSS ###
name: "DSS"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 500
input_dim: 500

# ------------------------ conv1 -----------------------------
layer {
    bottom: "data"
    top: "conv1"
    name: "conv1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 7
        pad: 3
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "conv1"
    top: "conv1"
    name: "bn_conv1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "conv1"
    top: "conv1"
    name: "scale_conv1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "conv1"
    bottom: "conv1"
    name: "conv1_relu"
    type: "ReLU"
}

layer {
    bottom: "conv1"
    top: "pool1"
    name: "pool1"
    type: "Pooling"
    pooling_param {
        kernel_size: 3
        stride: 2
        pool: MAX
    }
}

layer {
    bottom: "pool1"
    top: "res2a_branch1"
    name: "res2a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "bn2a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "scale2a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "pool1"
    top: "res2a_branch2a"
    name: "res2a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "bn2a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "scale2a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2a_branch2a"
    bottom: "res2a_branch2a"
    name: "res2a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2b"
    name: "res2a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "bn2a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "scale2a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2a_branch2b"
    bottom: "res2a_branch2b"
    name: "res2a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2c"
    name: "res2a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2c"
    top: "res2a_branch2c"
    name: "bn2a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2c"
    top: "res2a_branch2c"
    name: "scale2a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch1"
    bottom: "res2a_branch2c"
    top: "res2a"
    name: "res2a"
    type: "Eltwise"
}

layer {
    bottom: "res2a"
    top: "res2a"
    name: "res2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a"
    top: "res2b_branch2a"
    name: "res2b_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "bn2b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "scale2b_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2b_branch2a"
    bottom: "res2b_branch2a"
    name: "res2b_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2b"
    name: "res2b_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "bn2b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "scale2b_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2b_branch2b"
    bottom: "res2b_branch2b"
    name: "res2b_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2c"
    name: "res2b_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2c"
    top: "res2b_branch2c"
    name: "bn2b_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2c"
    top: "res2b_branch2c"
    name: "scale2b_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a"
    bottom: "res2b_branch2c"
    top: "res2b"
    name: "res2b"
    type: "Eltwise"
}

layer {
    bottom: "res2b"
    top: "res2b"
    name: "res2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b"
    top: "res2c_branch2a"
    name: "res2c_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2a"
    name: "bn2c_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2a"
    name: "scale2c_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2c_branch2a"
    bottom: "res2c_branch2a"
    name: "res2c_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2b"
    name: "res2c_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2b"
    name: "bn2c_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2b"
    name: "scale2c_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2c_branch2b"
    bottom: "res2c_branch2b"
    name: "res2c_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2c"
    name: "res2c_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2c"
    top: "res2c_branch2c"
    name: "bn2c_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2c"
    top: "res2c_branch2c"
    name: "scale2c_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b"
    bottom: "res2c_branch2c"
    top: "res2c"
    name: "res2c"
    type: "Eltwise"
}

layer {
    bottom: "res2c"
    top: "res2c"
    name: "res2c_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c"
    top: "res3a_branch1"
    name: "res3a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "bn3a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "scale3a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c"
    top: "res3a_branch2a"
    name: "res3a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "bn3a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "scale3a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3a_branch2a"
    bottom: "res3a_branch2a"
    name: "res3a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2b"
    name: "res3a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "bn3a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "scale3a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3a_branch2b"
    bottom: "res3a_branch2b"
    name: "res3a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2c"
    name: "res3a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2c"
    top: "res3a_branch2c"
    name: "bn3a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2c"
    top: "res3a_branch2c"
    name: "scale3a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch1"
    bottom: "res3a_branch2c"
    top: "res3a"
    name: "res3a"
    type: "Eltwise"
}

layer {
    bottom: "res3a"
    top: "res3a"
    name: "res3a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a"
    top: "res3b1_branch2a"
    name: "res3b1_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2a"
    name: "bn3b1_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2a"
    name: "scale3b1_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b1_branch2a"
    bottom: "res3b1_branch2a"
    name: "res3b1_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2b"
    name: "res3b1_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2b"
    name: "bn3b1_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2b"
    name: "scale3b1_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b1_branch2b"
    bottom: "res3b1_branch2b"
    name: "res3b1_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2c"
    name: "res3b1_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2c"
    top: "res3b1_branch2c"
    name: "bn3b1_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2c"
    top: "res3b1_branch2c"
    name: "scale3b1_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a"
    bottom: "res3b1_branch2c"
    top: "res3b1"
    name: "res3b1"
    type: "Eltwise"
}

layer {
    bottom: "res3b1"
    top: "res3b1"
    name: "res3b1_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1"
    top: "res3b2_branch2a"
    name: "res3b2_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2a"
    name: "bn3b2_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2a"
    name: "scale3b2_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b2_branch2a"
    bottom: "res3b2_branch2a"
    name: "res3b2_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2b"
    name: "res3b2_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2b"
    name: "bn3b2_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2b"
    name: "scale3b2_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b2_branch2b"
    bottom: "res3b2_branch2b"
    name: "res3b2_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2c"
    name: "res3b2_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2c"
    top: "res3b2_branch2c"
    name: "bn3b2_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2c"
    top: "res3b2_branch2c"
    name: "scale3b2_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1"
    bottom: "res3b2_branch2c"
    top: "res3b2"
    name: "res3b2"
    type: "Eltwise"
}

layer {
    bottom: "res3b2"
    top: "res3b2"
    name: "res3b2_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2"
    top: "res3b3_branch2a"
    name: "res3b3_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2a"
    name: "bn3b3_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2a"
    name: "scale3b3_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b3_branch2a"
    bottom: "res3b3_branch2a"
    name: "res3b3_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2b"
    name: "res3b3_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2b"
    name: "bn3b3_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2b"
    name: "scale3b3_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b3_branch2b"
    bottom: "res3b3_branch2b"
    name: "res3b3_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2c"
    name: "res3b3_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2c"
    top: "res3b3_branch2c"
    name: "bn3b3_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2c"
    top: "res3b3_branch2c"
    name: "scale3b3_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2"
    bottom: "res3b3_branch2c"
    top: "res3b3"
    name: "res3b3"
    type: "Eltwise"
}

layer {
    bottom: "res3b3"
    top: "res3b3"
    name: "res3b3_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3"
    top: "res4a_branch1"
    name: "res4a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "bn4a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "scale4a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3"
    top: "res4a_branch2a"
    name: "res4a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "bn4a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "scale4a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4a_branch2a"
    bottom: "res4a_branch2a"
    name: "res4a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2b"
    name: "res4a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "bn4a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "scale4a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4a_branch2b"
    bottom: "res4a_branch2b"
    name: "res4a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2c"
    name: "res4a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2c"
    top: "res4a_branch2c"
    name: "bn4a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2c"
    top: "res4a_branch2c"
    name: "scale4a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch1"
    bottom: "res4a_branch2c"
    top: "res4a"
    name: "res4a"
    type: "Eltwise"
}

layer {
    bottom: "res4a"
    top: "res4a"
    name: "res4a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a"
    top: "res4b1_branch2a"
    name: "res4b1_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2a"
    name: "bn4b1_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2a"
    name: "scale4b1_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b1_branch2a"
    bottom: "res4b1_branch2a"
    name: "res4b1_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2b"
    name: "res4b1_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2b"
    name: "bn4b1_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2b"
    name: "scale4b1_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b1_branch2b"
    bottom: "res4b1_branch2b"
    name: "res4b1_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2c"
    name: "res4b1_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2c"
    top: "res4b1_branch2c"
    name: "bn4b1_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2c"
    top: "res4b1_branch2c"
    name: "scale4b1_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a"
    bottom: "res4b1_branch2c"
    top: "res4b1"
    name: "res4b1"
    type: "Eltwise"
}

layer {
    bottom: "res4b1"
    top: "res4b1"
    name: "res4b1_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1"
    top: "res4b2_branch2a"
    name: "res4b2_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2a"
    name: "bn4b2_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2a"
    name: "scale4b2_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b2_branch2a"
    bottom: "res4b2_branch2a"
    name: "res4b2_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2b"
    name: "res4b2_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2b"
    name: "bn4b2_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2b"
    name: "scale4b2_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b2_branch2b"
    bottom: "res4b2_branch2b"
    name: "res4b2_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2c"
    name: "res4b2_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2c"
    top: "res4b2_branch2c"
    name: "bn4b2_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2c"
    top: "res4b2_branch2c"
    name: "scale4b2_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1"
    bottom: "res4b2_branch2c"
    top: "res4b2"
    name: "res4b2"
    type: "Eltwise"
}

layer {
    bottom: "res4b2"
    top: "res4b2"
    name: "res4b2_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2"
    top: "res4b3_branch2a"
    name: "res4b3_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2a"
    name: "bn4b3_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2a"
    name: "scale4b3_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b3_branch2a"
    bottom: "res4b3_branch2a"
    name: "res4b3_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2b"
    name: "res4b3_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2b"
    name: "bn4b3_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2b"
    name: "scale4b3_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b3_branch2b"
    bottom: "res4b3_branch2b"
    name: "res4b3_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2c"
    name: "res4b3_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2c"
    top: "res4b3_branch2c"
    name: "bn4b3_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2c"
    top: "res4b3_branch2c"
    name: "scale4b3_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2"
    bottom: "res4b3_branch2c"
    top: "res4b3"
    name: "res4b3"
    type: "Eltwise"
}

layer {
    bottom: "res4b3"
    top: "res4b3"
    name: "res4b3_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3"
    top: "res4b4_branch2a"
    name: "res4b4_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2a"
    name: "bn4b4_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2a"
    name: "scale4b4_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b4_branch2a"
    bottom: "res4b4_branch2a"
    name: "res4b4_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2b"
    name: "res4b4_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2b"
    name: "bn4b4_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2b"
    name: "scale4b4_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b4_branch2b"
    bottom: "res4b4_branch2b"
    name: "res4b4_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2c"
    name: "res4b4_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2c"
    top: "res4b4_branch2c"
    name: "bn4b4_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2c"
    top: "res4b4_branch2c"
    name: "scale4b4_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3"
    bottom: "res4b4_branch2c"
    top: "res4b4"
    name: "res4b4"
    type: "Eltwise"
}

layer {
    bottom: "res4b4"
    top: "res4b4"
    name: "res4b4_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4"
    top: "res4b5_branch2a"
    name: "res4b5_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2a"
    name: "bn4b5_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2a"
    name: "scale4b5_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b5_branch2a"
    bottom: "res4b5_branch2a"
    name: "res4b5_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2b"
    name: "res4b5_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2b"
    name: "bn4b5_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2b"
    name: "scale4b5_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b5_branch2b"
    bottom: "res4b5_branch2b"
    name: "res4b5_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2c"
    name: "res4b5_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2c"
    top: "res4b5_branch2c"
    name: "bn4b5_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2c"
    top: "res4b5_branch2c"
    name: "scale4b5_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4"
    bottom: "res4b5_branch2c"
    top: "res4b5"
    name: "res4b5"
    type: "Eltwise"
}

layer {
    bottom: "res4b5"
    top: "res4b5"
    name: "res4b5_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5"
    top: "res4b6_branch2a"
    name: "res4b6_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2a"
    name: "bn4b6_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2a"
    name: "scale4b6_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b6_branch2a"
    bottom: "res4b6_branch2a"
    name: "res4b6_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2b"
    name: "res4b6_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2b"
    name: "bn4b6_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2b"
    name: "scale4b6_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b6_branch2b"
    bottom: "res4b6_branch2b"
    name: "res4b6_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2c"
    name: "res4b6_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2c"
    top: "res4b6_branch2c"
    name: "bn4b6_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2c"
    top: "res4b6_branch2c"
    name: "scale4b6_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5"
    bottom: "res4b6_branch2c"
    top: "res4b6"
    name: "res4b6"
    type: "Eltwise"
}

layer {
    bottom: "res4b6"
    top: "res4b6"
    name: "res4b6_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6"
    top: "res4b7_branch2a"
    name: "res4b7_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2a"
    name: "bn4b7_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2a"
    name: "scale4b7_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b7_branch2a"
    bottom: "res4b7_branch2a"
    name: "res4b7_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2b"
    name: "res4b7_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2b"
    name: "bn4b7_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2b"
    name: "scale4b7_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b7_branch2b"
    bottom: "res4b7_branch2b"
    name: "res4b7_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2c"
    name: "res4b7_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2c"
    top: "res4b7_branch2c"
    name: "bn4b7_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2c"
    top: "res4b7_branch2c"
    name: "scale4b7_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6"
    bottom: "res4b7_branch2c"
    top: "res4b7"
    name: "res4b7"
    type: "Eltwise"
}

layer {
    bottom: "res4b7"
    top: "res4b7"
    name: "res4b7_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7"
    top: "res4b8_branch2a"
    name: "res4b8_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2a"
    name: "bn4b8_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2a"
    name: "scale4b8_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b8_branch2a"
    bottom: "res4b8_branch2a"
    name: "res4b8_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2b"
    name: "res4b8_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2b"
    name: "bn4b8_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2b"
    name: "scale4b8_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b8_branch2b"
    bottom: "res4b8_branch2b"
    name: "res4b8_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2c"
    name: "res4b8_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2c"
    top: "res4b8_branch2c"
    name: "bn4b8_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2c"
    top: "res4b8_branch2c"
    name: "scale4b8_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7"
    bottom: "res4b8_branch2c"
    top: "res4b8"
    name: "res4b8"
    type: "Eltwise"
}

layer {
    bottom: "res4b8"
    top: "res4b8"
    name: "res4b8_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8"
    top: "res4b9_branch2a"
    name: "res4b9_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2a"
    name: "bn4b9_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2a"
    name: "scale4b9_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b9_branch2a"
    bottom: "res4b9_branch2a"
    name: "res4b9_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2b"
    name: "res4b9_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2b"
    name: "bn4b9_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2b"
    name: "scale4b9_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b9_branch2b"
    bottom: "res4b9_branch2b"
    name: "res4b9_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2c"
    name: "res4b9_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2c"
    top: "res4b9_branch2c"
    name: "bn4b9_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2c"
    top: "res4b9_branch2c"
    name: "scale4b9_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8"
    bottom: "res4b9_branch2c"
    top: "res4b9"
    name: "res4b9"
    type: "Eltwise"
}

layer {
    bottom: "res4b9"
    top: "res4b9"
    name: "res4b9_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9"
    top: "res4b10_branch2a"
    name: "res4b10_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2a"
    name: "bn4b10_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2a"
    name: "scale4b10_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b10_branch2a"
    bottom: "res4b10_branch2a"
    name: "res4b10_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2b"
    name: "res4b10_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2b"
    name: "bn4b10_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2b"
    name: "scale4b10_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b10_branch2b"
    bottom: "res4b10_branch2b"
    name: "res4b10_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2c"
    name: "res4b10_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2c"
    top: "res4b10_branch2c"
    name: "bn4b10_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2c"
    top: "res4b10_branch2c"
    name: "scale4b10_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9"
    bottom: "res4b10_branch2c"
    top: "res4b10"
    name: "res4b10"
    type: "Eltwise"
}

layer {
    bottom: "res4b10"
    top: "res4b10"
    name: "res4b10_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10"
    top: "res4b11_branch2a"
    name: "res4b11_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2a"
    name: "bn4b11_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2a"
    name: "scale4b11_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b11_branch2a"
    bottom: "res4b11_branch2a"
    name: "res4b11_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2b"
    name: "res4b11_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2b"
    name: "bn4b11_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2b"
    name: "scale4b11_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b11_branch2b"
    bottom: "res4b11_branch2b"
    name: "res4b11_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2c"
    name: "res4b11_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2c"
    top: "res4b11_branch2c"
    name: "bn4b11_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2c"
    top: "res4b11_branch2c"
    name: "scale4b11_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10"
    bottom: "res4b11_branch2c"
    top: "res4b11"
    name: "res4b11"
    type: "Eltwise"
}

layer {
    bottom: "res4b11"
    top: "res4b11"
    name: "res4b11_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11"
    top: "res4b12_branch2a"
    name: "res4b12_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2a"
    name: "bn4b12_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2a"
    name: "scale4b12_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b12_branch2a"
    bottom: "res4b12_branch2a"
    name: "res4b12_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2b"
    name: "res4b12_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2b"
    name: "bn4b12_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2b"
    name: "scale4b12_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b12_branch2b"
    bottom: "res4b12_branch2b"
    name: "res4b12_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2c"
    name: "res4b12_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2c"
    top: "res4b12_branch2c"
    name: "bn4b12_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2c"
    top: "res4b12_branch2c"
    name: "scale4b12_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11"
    bottom: "res4b12_branch2c"
    top: "res4b12"
    name: "res4b12"
    type: "Eltwise"
}

layer {
    bottom: "res4b12"
    top: "res4b12"
    name: "res4b12_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12"
    top: "res4b13_branch2a"
    name: "res4b13_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2a"
    name: "bn4b13_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2a"
    name: "scale4b13_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b13_branch2a"
    bottom: "res4b13_branch2a"
    name: "res4b13_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2b"
    name: "res4b13_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2b"
    name: "bn4b13_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2b"
    name: "scale4b13_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b13_branch2b"
    bottom: "res4b13_branch2b"
    name: "res4b13_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2c"
    name: "res4b13_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2c"
    top: "res4b13_branch2c"
    name: "bn4b13_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2c"
    top: "res4b13_branch2c"
    name: "scale4b13_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12"
    bottom: "res4b13_branch2c"
    top: "res4b13"
    name: "res4b13"
    type: "Eltwise"
}

layer {
    bottom: "res4b13"
    top: "res4b13"
    name: "res4b13_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13"
    top: "res4b14_branch2a"
    name: "res4b14_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2a"
    name: "bn4b14_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2a"
    name: "scale4b14_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b14_branch2a"
    bottom: "res4b14_branch2a"
    name: "res4b14_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2b"
    name: "res4b14_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2b"
    name: "bn4b14_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2b"
    name: "scale4b14_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b14_branch2b"
    bottom: "res4b14_branch2b"
    name: "res4b14_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2c"
    name: "res4b14_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2c"
    top: "res4b14_branch2c"
    name: "bn4b14_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2c"
    top: "res4b14_branch2c"
    name: "scale4b14_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13"
    bottom: "res4b14_branch2c"
    top: "res4b14"
    name: "res4b14"
    type: "Eltwise"
}

layer {
    bottom: "res4b14"
    top: "res4b14"
    name: "res4b14_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14"
    top: "res4b15_branch2a"
    name: "res4b15_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2a"
    name: "bn4b15_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2a"
    name: "scale4b15_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b15_branch2a"
    bottom: "res4b15_branch2a"
    name: "res4b15_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2b"
    name: "res4b15_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2b"
    name: "bn4b15_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2b"
    name: "scale4b15_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b15_branch2b"
    bottom: "res4b15_branch2b"
    name: "res4b15_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2c"
    name: "res4b15_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2c"
    top: "res4b15_branch2c"
    name: "bn4b15_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2c"
    top: "res4b15_branch2c"
    name: "scale4b15_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14"
    bottom: "res4b15_branch2c"
    top: "res4b15"
    name: "res4b15"
    type: "Eltwise"
}

layer {
    bottom: "res4b15"
    top: "res4b15"
    name: "res4b15_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15"
    top: "res4b16_branch2a"
    name: "res4b16_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2a"
    name: "bn4b16_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2a"
    name: "scale4b16_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b16_branch2a"
    bottom: "res4b16_branch2a"
    name: "res4b16_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2b"
    name: "res4b16_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2b"
    name: "bn4b16_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2b"
    name: "scale4b16_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b16_branch2b"
    bottom: "res4b16_branch2b"
    name: "res4b16_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2c"
    name: "res4b16_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2c"
    top: "res4b16_branch2c"
    name: "bn4b16_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2c"
    top: "res4b16_branch2c"
    name: "scale4b16_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15"
    bottom: "res4b16_branch2c"
    top: "res4b16"
    name: "res4b16"
    type: "Eltwise"
}

layer {
    bottom: "res4b16"
    top: "res4b16"
    name: "res4b16_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16"
    top: "res4b17_branch2a"
    name: "res4b17_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2a"
    name: "bn4b17_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2a"
    name: "scale4b17_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b17_branch2a"
    bottom: "res4b17_branch2a"
    name: "res4b17_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2b"
    name: "res4b17_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2b"
    name: "bn4b17_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2b"
    name: "scale4b17_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b17_branch2b"
    bottom: "res4b17_branch2b"
    name: "res4b17_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2c"
    name: "res4b17_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2c"
    top: "res4b17_branch2c"
    name: "bn4b17_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2c"
    top: "res4b17_branch2c"
    name: "scale4b17_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16"
    bottom: "res4b17_branch2c"
    top: "res4b17"
    name: "res4b17"
    type: "Eltwise"
}

layer {
    bottom: "res4b17"
    top: "res4b17"
    name: "res4b17_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17"
    top: "res4b18_branch2a"
    name: "res4b18_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2a"
    name: "bn4b18_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2a"
    name: "scale4b18_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b18_branch2a"
    bottom: "res4b18_branch2a"
    name: "res4b18_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2b"
    name: "res4b18_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2b"
    name: "bn4b18_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2b"
    name: "scale4b18_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b18_branch2b"
    bottom: "res4b18_branch2b"
    name: "res4b18_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2c"
    name: "res4b18_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2c"
    top: "res4b18_branch2c"
    name: "bn4b18_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2c"
    top: "res4b18_branch2c"
    name: "scale4b18_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17"
    bottom: "res4b18_branch2c"
    top: "res4b18"
    name: "res4b18"
    type: "Eltwise"
}

layer {
    bottom: "res4b18"
    top: "res4b18"
    name: "res4b18_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18"
    top: "res4b19_branch2a"
    name: "res4b19_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2a"
    name: "bn4b19_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2a"
    name: "scale4b19_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b19_branch2a"
    bottom: "res4b19_branch2a"
    name: "res4b19_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2b"
    name: "res4b19_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2b"
    name: "bn4b19_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2b"
    name: "scale4b19_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b19_branch2b"
    bottom: "res4b19_branch2b"
    name: "res4b19_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2c"
    name: "res4b19_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2c"
    top: "res4b19_branch2c"
    name: "bn4b19_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2c"
    top: "res4b19_branch2c"
    name: "scale4b19_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18"
    bottom: "res4b19_branch2c"
    top: "res4b19"
    name: "res4b19"
    type: "Eltwise"
}

layer {
    bottom: "res4b19"
    top: "res4b19"
    name: "res4b19_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19"
    top: "res4b20_branch2a"
    name: "res4b20_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2a"
    name: "bn4b20_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2a"
    name: "scale4b20_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b20_branch2a"
    bottom: "res4b20_branch2a"
    name: "res4b20_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2b"
    name: "res4b20_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2b"
    name: "bn4b20_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2b"
    name: "scale4b20_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b20_branch2b"
    bottom: "res4b20_branch2b"
    name: "res4b20_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2c"
    name: "res4b20_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2c"
    top: "res4b20_branch2c"
    name: "bn4b20_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2c"
    top: "res4b20_branch2c"
    name: "scale4b20_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19"
    bottom: "res4b20_branch2c"
    top: "res4b20"
    name: "res4b20"
    type: "Eltwise"
}

layer {
    bottom: "res4b20"
    top: "res4b20"
    name: "res4b20_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20"
    top: "res4b21_branch2a"
    name: "res4b21_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2a"
    name: "bn4b21_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2a"
    name: "scale4b21_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b21_branch2a"
    bottom: "res4b21_branch2a"
    name: "res4b21_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2b"
    name: "res4b21_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2b"
    name: "bn4b21_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2b"
    name: "scale4b21_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b21_branch2b"
    bottom: "res4b21_branch2b"
    name: "res4b21_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2c"
    name: "res4b21_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2c"
    top: "res4b21_branch2c"
    name: "bn4b21_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2c"
    top: "res4b21_branch2c"
    name: "scale4b21_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20"
    bottom: "res4b21_branch2c"
    top: "res4b21"
    name: "res4b21"
    type: "Eltwise"
}

layer {
    bottom: "res4b21"
    top: "res4b21"
    name: "res4b21_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21"
    top: "res4b22_branch2a"
    name: "res4b22_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2a"
    name: "bn4b22_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2a"
    name: "scale4b22_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b22_branch2a"
    bottom: "res4b22_branch2a"
    name: "res4b22_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2b"
    name: "res4b22_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2b"
    name: "bn4b22_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2b"
    name: "scale4b22_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b22_branch2b"
    bottom: "res4b22_branch2b"
    name: "res4b22_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2c"
    name: "res4b22_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2c"
    top: "res4b22_branch2c"
    name: "bn4b22_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2c"
    top: "res4b22_branch2c"
    name: "scale4b22_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21"
    bottom: "res4b22_branch2c"
    top: "res4b22"
    name: "res4b22"
    type: "Eltwise"
}

layer {
    bottom: "res4b22"
    top: "res4b22"
    name: "res4b22_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22"
    top: "res5a_branch1"
    name: "res5a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "bn5a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "scale5a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22"
    top: "res5a_branch2a"
    name: "res5a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "bn5a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "scale5a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5a_branch2a"
    bottom: "res5a_branch2a"
    name: "res5a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2b"
    name: "res5a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "bn5a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "scale5a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5a_branch2b"
    bottom: "res5a_branch2b"
    name: "res5a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2c"
    name: "res5a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2c"
    top: "res5a_branch2c"
    name: "bn5a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2c"
    top: "res5a_branch2c"
    name: "scale5a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch1"
    bottom: "res5a_branch2c"
    top: "res5a"
    name: "res5a"
    type: "Eltwise"
}

layer {
    bottom: "res5a"
    top: "res5a"
    name: "res5a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a"
    top: "res5b_branch2a"
    name: "res5b_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "bn5b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "scale5b_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5b_branch2a"
    bottom: "res5b_branch2a"
    name: "res5b_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2b"
    name: "res5b_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "bn5b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "scale5b_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5b_branch2b"
    bottom: "res5b_branch2b"
    name: "res5b_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2c"
    name: "res5b_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2c"
    top: "res5b_branch2c"
    name: "bn5b_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2c"
    top: "res5b_branch2c"
    name: "scale5b_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a"
    bottom: "res5b_branch2c"
    top: "res5b"
    name: "res5b"
    type: "Eltwise"
}

layer {
    bottom: "res5b"
    top: "res5b"
    name: "res5b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b"
    top: "res5c_branch2a"
    name: "res5c_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2a"
    name: "bn5c_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2a"
    name: "scale5c_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5c_branch2a"
    bottom: "res5c_branch2a"
    name: "res5c_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2b"
    name: "res5c_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2b"
    name: "bn5c_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2b"
    name: "scale5c_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5c_branch2b"
    bottom: "res5c_branch2b"
    name: "res5c_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2c"
    name: "res5c_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2c"
    top: "res5c_branch2c"
    name: "bn5c_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2c"
    top: "res5c_branch2c"
    name: "scale5c_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b"
    bottom: "res5c_branch2c"
    top: "res5c"
    name: "res5c"
    type: "Eltwise"
}

layer {
    bottom: "res5c"
    top: "res5c"
    name: "res5c_relu"
    type: "ReLU"
}

###DSN conv 6###
layer { name: 'conv1-dsn6' type: "Convolution" bottom: 'res5c' top: 'conv1-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn6' top: 'conv1-dsn6' name: 'relu1-dsn6' type: "ReLU" }
layer { name: 'conv2-dsn6' type: "Convolution" bottom: 'conv1-dsn6' top: 'conv2-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn6' top: 'conv2-dsn6' name: 'relu2-dsn6' type: "ReLU" }
layer { name: 'conv3-dsn6' type: "Convolution" bottom: 'conv2-dsn6' top: 'conv3-dsn6'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_in_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up' bottom: 'data' top: 'upscore-dsn6' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn6" bottom: "label" top: "loss-dsn6"
  name: "loss-dsn6" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn6" bottom: "upscore-dsn6" top: "sigmoid-dsn6" include { phase: TEST} }

###DSN conv 5###
layer { name: 'conv1-dsn5' type: "Convolution" bottom: 'res4b22' top: 'conv1-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn5' top: 'conv1-dsn5' name: 'relu1-dsn5' type: "ReLU" }
layer { name: 'conv2-dsn5' type: "Convolution" bottom: 'conv1-dsn5' top: 'conv2-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn5' top: 'conv2-dsn5' name: 'relu2-dsn5' type: "ReLU" }
layer { name: 'conv3-dsn5' type: "Convolution" bottom: 'conv2-dsn5' top: 'conv3-dsn5'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_in_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top: "loss-dsn5"
  name: "loss-dsn5" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn5" bottom: "upscore-dsn5" top: "sigmoid-dsn5" include { phase: TEST} }

###DSN conv 4###
layer { name: 'conv1-dsn4' type: "Convolution" bottom: 'res3b3' top: 'conv1-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn4' top: 'conv1-dsn4' name: 'relu1-dsn4' type: "ReLU" }
layer { name: 'conv2-dsn4' type: "Convolution" bottom: 'conv1-dsn4' top: 'conv2-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn4' top: 'conv2-dsn4' name: 'relu2-dsn4' type: "ReLU" }
layer { name: 'conv3-dsn4' type: "Convolution" bottom: 'conv2-dsn4' top: 'conv3-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn6-4' }
layer { type: "Deconvolution" name: 'upsample2_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn5-4' }
layer { name: "concat-dsn4" bottom: "conv3-dsn4"  bottom: "upscore-dsn6-4" bottom: "upscore-dsn5-4" 
  top: "concat-dsn4" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn4' type: "Convolution" bottom: 'concat-dsn4' top: 'conv4-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_in_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top: "loss-dsn4"
  name: "loss-dsn4" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn4" bottom: "upscore-dsn4" top: "sigmoid-dsn4" include { phase: TEST} }

### DSN conv 3 ###
layer { name: 'conv1-dsn3' type: "Convolution" bottom: 'res2c' top: 'conv1-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn3' top: 'conv1-dsn3' name: 'relu1-dsn3' type: "ReLU" }
layer { name: 'conv2-dsn3' type: "Convolution" bottom: 'conv1-dsn3' top: 'conv2-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn3' top: 'conv2-dsn3' name: 'relu2-dsn3' type: "ReLU" }
layer { name: 'conv3-dsn3' type: "Convolution" bottom: 'conv2-dsn3' top: 'conv3-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn6-3' }
layer { type: "Deconvolution" name: 'upsample4_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn5-3' }
layer { name: "concat-dsn3" bottom: "conv3-dsn3"  bottom: "upscore-dsn6-3" bottom: "upscore-dsn5-3" 
  top: "concat-dsn3" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn3' type: "Convolution" bottom: 'concat-dsn3' top: 'conv4-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_in_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top: "loss-dsn3"
  name: "loss-dsn3" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn3" bottom: "upscore-dsn3" top: "sigmoid-dsn3" include { phase: TEST} }

### DSN conv 2 ###
layer { name: 'conv1-dsn2' type: "Convolution" bottom: 'conv1' top: 'conv1-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn2' top: 'conv1-dsn2' name: 'relu1-dsn2' type: "ReLU" }
layer { name: 'conv2-dsn2' type: "Convolution" bottom: 'conv1-dsn2' top: 'conv2-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn2' top: 'conv2-dsn2' name: 'relu2-dsn2' type: "ReLU" }
layer { name: 'conv3-dsn2' type: "Convolution" bottom: 'conv2-dsn2' top: 'conv3-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn6-2' }
layer { type: "Deconvolution" name: 'upsample8_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn5-2' }
layer { type: "Deconvolution" name: 'upsample4_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn4-2' }
layer { type: "Deconvolution" name: 'upsample2_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn3-2' }
layer { name: "concat-dsn2" bottom: "conv3-dsn2" bottom: "upscore-dsn5-2" bottom: "upscore-dsn4-2" bottom: "upscore-dsn6-2"
  bottom: "upscore-dsn3-2" top: "concat-dsn2" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn2' type: "Convolution" bottom: 'concat-dsn2' top: 'conv4-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample2_in_dsn2' bottom: 'conv4-dsn2' top: 'score-dsn2-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top: "loss-dsn2"
  name: "loss-dsn2" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn2" bottom: "upscore-dsn2" top: "sigmoid-dsn2" include { phase: TEST} }

### Concat and multiscale weight layer ###
layer { name: "concat" bottom: "upscore-dsn6"  bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" 
  bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat" concat_param { concat_dim: 1 } }
layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.2 } } }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top: "loss-fuse"
  name: "loss-fuse" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-fuse" bottom: "upscore-fuse" top: "sigmoid-fuse" include { phase: TEST } }


================================================
FILE: resnet/sal_data_layer.py
================================================
import sys
caffe_root = '../../'
sys.path.insert(0, caffe_root + 'python')
import caffe

import numpy as np
from PIL import Image
import scipy.io as io
import cv2

import random

class SalDataLayer(caffe.Layer):
    """
    Load (input image, label image) pairs from PASCAL VOC
    one-at-a-time while reshaping the net to preserve dimensions.
    Use this to feed data to a fully convolutional network.
    """

    def setup(self, bottom, top):
        """
        Setup data layer according to parameters:
        - voc_dir: path to PASCAL VOC year dir
        - split: train / val / test
        - mean: tuple of mean values to subtract
        - randomize: load in random order (default: True)
        - seed: seed for randomization (default: None / current time)
        for PASCAL VOC semantic segmentation.
        example
        params = dict(voc_dir="/path/to/PASCAL/VOC2011",
            mean=(104.00698793, 116.66876762, 122.67891434),
            split="val")
        """
        # config
        params = eval(self.param_str)
        self.data_root = params.get('data_root', '')
        self.source = '/Data/Existence/train.lst'
        #self.data_root = params.get('data_root', '/Data/instance/sal_inst/single/')
        #self.source = '/Data/instance/sal_inst/train.lst'
        self.random = True

        #self.source = params.get('source', '/Data/weak_seg/low-level/VOC2012/val_region.txt')
        self.mean = np.array(params.get('mean', [104.070, 116.669, 122.679]))
        self.seed = params.get('seed', None)
        self.seed = random.randint(0, 1024)
        #self.scales = params.get('scales', [0.5, 0.75, 1, 1.5])

        # two tops: data and label
        #if len(top) != 3: # image + pixel-level annos + estimated regions
        #    raise Exception("Need to define two tops: data and 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  = '{}/ImageSets/Segmentation/{}.txt'.format(self.voc_dir, self.split)
        self.indices = open(self.source, 'r').read().splitlines()
        print 'Totally {} training images'.format(len(self.indices))
        self.idx = 0
        self.image_lst = [x.split()[0] for x in self.indices]
        self.label_lst = [x.split()[1] for x in self.indices]
        self.sal_lst = [x.split()[2] for x in self.indices]

        # 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)

        self.flip = 1


    def reshape(self, bottom, top):
        #if self.scales != None:
        #    self.scale_ind = random.randint(0, len(self.scales)-1)

        self.flip = random.randint(0, 1)

        # load image + label image pair
        self.data = self.load_image(self.idx)
        self.label = self.load_label(self.idx)
        self.sal = int(self.sal_lst[self.idx])

        # random crop
        #height, width = self.data.shape[-2:]
        #h_start = random.randint(0, np.abs(self.crop_size - height))
        #w_start = random.randint(0, np.abs(self.crop_size - width))
        #self.data = self.data[:, h_start:h_start+self.crop_size, w_start:w_start+self.crop_size]
        #self.label = self.label[:, h_start:h_start+self.crop_size, w_start:w_start+self.crop_size]

        # reshape tops to fit (leading 1 is for batch dimension)
        top[0].reshape(1, *self.data.shape)
        top[1].reshape(1, *self.label.shape)
        top[2].reshape(1, 1, 1, 1)


    def forward(self, bottom, top):
        # assign output
        top[0].data[...] = self.data
        top[1].data[...] = self.label
        top[2].data[...] = self.sal

        # 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 = cv2.imread(self.data_root + self.image_lst[idx], cv2.IMREAD_COLOR)
        #im = Image.open(self.data_root + self.image_lst[idx])
        im = np.array(im, dtype=np.float32)
        #self.height, self.width = im.shape[:2]
        #if self.scales != None:
        #    im = cv2.resize(im, None, None, fx=self.scales[self.scale_ind], fy=self.scales[self.scale_ind], interpolation=cv2.INTER_CUBIC)

        im = im[:,:,::-1]
        im -= self.mean
        #self.h_off = random.randint(0, int(self.scales[self.scale_ind] * self.height - self.height))
        #self.w_off = random.randint(0, int(self.scales[self.scale_ind] * self.width - self.width))
        #in_ = cv2.copyMakeBorder(in_, 0, max(0, h_off), 0, max(0, w_off), cv2.BORDER_CONSTANT, value=[0,0,0])
        #im = im[self.h_off:self.h_off+self.height, self.w_off:self.w_off+self.width, :]
        im = im.transpose((2,0,1))

        if self.flip == 1:
            im = im[:,:,::-1]
        return im


    def load_label(self, idx):
        """
        Load label image as 1 x height x width integer array of label indices.
        The leading singleton dimension is required by the loss.
        """
        im = Image.open(self.data_root + self.label_lst[idx])
        label = np.array(im) / 255#cv2.imread(self.data_root + self.label_lst[idx], 0) / 255
        #if self.scales != None:
        #    label = cv2.resize(label, None, None, fx=self.scales[self.scale_ind], fy=self.scales[self.scale_ind], \
        #            interpolation=cv2.INTER_NEAREST)
        #height, width = label.shape[:2]
        #h_off = self.crop_size - height
        #w_off = self.crop_size - width
        #label = cv2.copyMakeBorder(label, 0, max(0, h_off), 0, max(0, w_off), cv2.BORDER_CONSTANT, value=[-1,])
        #label = label[self.h_off:self.h_off+self.height, self.w_off:self.w_off+self.width]
        label = label[np.newaxis, ...]
        if self.flip == 1:
            label = label[:,:,::-1]
        return label

    def load_region(self, idx):
        """
        Load label image as 1 x height x width integer array of label indices.
        The leading singleton dimension is required by the loss.
        """
        im = Image.open(self.data_root + self.region_lst[idx])
        region = np.array(im, dtype=np.float32) / 15.0
        #print np.unique(region)
        #if self.scales != None:
        #    label = cv2.resize(label, None, None, fx=self.scales[self.scale_ind], fy=self.scales[self.scale_ind], \
        #            interpolation=cv2.INTER_NEAREST)
        #height, width = label.shape[:2]
        #h_off = self.crop_size - height
        #w_off = self.crop_size - width
        #label = cv2.copyMakeBorder(label, 0, max(0, h_off), 0, max(0, w_off), cv2.BORDER_CONSTANT, value=[-1,])
        region = region[np.newaxis, ...]
        if self.flip == 1:
            region = region[:,:,::-1]
        return region


================================================
FILE: resnet/train_resnet.prototxt
================================================
### DSS ###
name: "DSS"
layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include { phase: TRAIN }
  transform_param {
    mirror: true
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  image_data_param {
    root_folder: "/Data/saliency/MSRA-B/"
    source: "/Data/saliency/msrab.lst"
    batch_size: 1
    shuffle: true
    normalize: true
  }
}

# ------------------------ conv1 -----------------------------
layer {
    bottom: "data"
    top: "conv1"
    name: "conv1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 7
        pad: 3
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "conv1"
    top: "conv1"
    name: "bn_conv1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "conv1"
    top: "conv1"
    name: "scale_conv1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "conv1"
    bottom: "conv1"
    name: "conv1_relu"
    type: "ReLU"
}

layer {
    bottom: "conv1"
    top: "pool1"
    name: "pool1"
    type: "Pooling"
    pooling_param {
        kernel_size: 3
        stride: 2
        pool: MAX
    }
}

layer {
    bottom: "pool1"
    top: "res2a_branch1"
    name: "res2a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "bn2a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "scale2a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "pool1"
    top: "res2a_branch2a"
    name: "res2a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "bn2a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "scale2a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2a_branch2a"
    bottom: "res2a_branch2a"
    name: "res2a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a_branch2a"
    top: "res2a_branch2b"
    name: "res2a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "bn2a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "scale2a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2a_branch2b"
    bottom: "res2a_branch2b"
    name: "res2a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a_branch2b"
    top: "res2a_branch2c"
    name: "res2a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2a_branch2c"
    top: "res2a_branch2c"
    name: "bn2a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch2c"
    top: "res2a_branch2c"
    name: "scale2a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a_branch1"
    bottom: "res2a_branch2c"
    top: "res2a"
    name: "res2a"
    type: "Eltwise"
}

layer {
    bottom: "res2a"
    top: "res2a"
    name: "res2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2a"
    top: "res2b_branch2a"
    name: "res2b_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "bn2b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "scale2b_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2b_branch2a"
    bottom: "res2b_branch2a"
    name: "res2b_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b_branch2a"
    top: "res2b_branch2b"
    name: "res2b_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "bn2b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "scale2b_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2b_branch2b"
    bottom: "res2b_branch2b"
    name: "res2b_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b_branch2b"
    top: "res2b_branch2c"
    name: "res2b_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2b_branch2c"
    top: "res2b_branch2c"
    name: "bn2b_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b_branch2c"
    top: "res2b_branch2c"
    name: "scale2b_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2a"
    bottom: "res2b_branch2c"
    top: "res2b"
    name: "res2b"
    type: "Eltwise"
}

layer {
    bottom: "res2b"
    top: "res2b"
    name: "res2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2b"
    top: "res2c_branch2a"
    name: "res2c_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2a"
    name: "bn2c_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2a"
    name: "scale2c_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2c_branch2a"
    bottom: "res2c_branch2a"
    name: "res2c_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c_branch2a"
    top: "res2c_branch2b"
    name: "res2c_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2b"
    name: "bn2c_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2b"
    name: "scale2c_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res2c_branch2b"
    bottom: "res2c_branch2b"
    name: "res2c_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c_branch2b"
    top: "res2c_branch2c"
    name: "res2c_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res2c_branch2c"
    top: "res2c_branch2c"
    name: "bn2c_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c_branch2c"
    top: "res2c_branch2c"
    name: "scale2c_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2b"
    bottom: "res2c_branch2c"
    top: "res2c"
    name: "res2c"
    type: "Eltwise"
}

layer {
    bottom: "res2c"
    top: "res2c"
    name: "res2c_relu"
    type: "ReLU"
}

layer {
    bottom: "res2c"
    top: "res3a_branch1"
    name: "res3a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "bn3a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "scale3a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res2c"
    top: "res3a_branch2a"
    name: "res3a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "bn3a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "scale3a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3a_branch2a"
    bottom: "res3a_branch2a"
    name: "res3a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a_branch2a"
    top: "res3a_branch2b"
    name: "res3a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "bn3a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "scale3a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3a_branch2b"
    bottom: "res3a_branch2b"
    name: "res3a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a_branch2b"
    top: "res3a_branch2c"
    name: "res3a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3a_branch2c"
    top: "res3a_branch2c"
    name: "bn3a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch2c"
    top: "res3a_branch2c"
    name: "scale3a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a_branch1"
    bottom: "res3a_branch2c"
    top: "res3a"
    name: "res3a"
    type: "Eltwise"
}

layer {
    bottom: "res3a"
    top: "res3a"
    name: "res3a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3a"
    top: "res3b1_branch2a"
    name: "res3b1_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2a"
    name: "bn3b1_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2a"
    name: "scale3b1_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b1_branch2a"
    bottom: "res3b1_branch2a"
    name: "res3b1_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1_branch2a"
    top: "res3b1_branch2b"
    name: "res3b1_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2b"
    name: "bn3b1_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2b"
    name: "scale3b1_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b1_branch2b"
    bottom: "res3b1_branch2b"
    name: "res3b1_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1_branch2b"
    top: "res3b1_branch2c"
    name: "res3b1_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b1_branch2c"
    top: "res3b1_branch2c"
    name: "bn3b1_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1_branch2c"
    top: "res3b1_branch2c"
    name: "scale3b1_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3a"
    bottom: "res3b1_branch2c"
    top: "res3b1"
    name: "res3b1"
    type: "Eltwise"
}

layer {
    bottom: "res3b1"
    top: "res3b1"
    name: "res3b1_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b1"
    top: "res3b2_branch2a"
    name: "res3b2_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2a"
    name: "bn3b2_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2a"
    name: "scale3b2_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b2_branch2a"
    bottom: "res3b2_branch2a"
    name: "res3b2_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2_branch2a"
    top: "res3b2_branch2b"
    name: "res3b2_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2b"
    name: "bn3b2_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2b"
    name: "scale3b2_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b2_branch2b"
    bottom: "res3b2_branch2b"
    name: "res3b2_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2_branch2b"
    top: "res3b2_branch2c"
    name: "res3b2_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b2_branch2c"
    top: "res3b2_branch2c"
    name: "bn3b2_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2_branch2c"
    top: "res3b2_branch2c"
    name: "scale3b2_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b1"
    bottom: "res3b2_branch2c"
    top: "res3b2"
    name: "res3b2"
    type: "Eltwise"
}

layer {
    bottom: "res3b2"
    top: "res3b2"
    name: "res3b2_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b2"
    top: "res3b3_branch2a"
    name: "res3b3_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2a"
    name: "bn3b3_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2a"
    name: "scale3b3_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b3_branch2a"
    bottom: "res3b3_branch2a"
    name: "res3b3_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3_branch2a"
    top: "res3b3_branch2b"
    name: "res3b3_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2b"
    name: "bn3b3_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2b"
    name: "scale3b3_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res3b3_branch2b"
    bottom: "res3b3_branch2b"
    name: "res3b3_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3_branch2b"
    top: "res3b3_branch2c"
    name: "res3b3_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res3b3_branch2c"
    top: "res3b3_branch2c"
    name: "bn3b3_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3_branch2c"
    top: "res3b3_branch2c"
    name: "scale3b3_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b2"
    bottom: "res3b3_branch2c"
    top: "res3b3"
    name: "res3b3"
    type: "Eltwise"
}

layer {
    bottom: "res3b3"
    top: "res3b3"
    name: "res3b3_relu"
    type: "ReLU"
}

layer {
    bottom: "res3b3"
    top: "res4a_branch1"
    name: "res4a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "bn4a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "scale4a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res3b3"
    top: "res4a_branch2a"
    name: "res4a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 2
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "bn4a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "scale4a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4a_branch2a"
    bottom: "res4a_branch2a"
    name: "res4a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a_branch2a"
    top: "res4a_branch2b"
    name: "res4a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "bn4a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "scale4a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4a_branch2b"
    bottom: "res4a_branch2b"
    name: "res4a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a_branch2b"
    top: "res4a_branch2c"
    name: "res4a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4a_branch2c"
    top: "res4a_branch2c"
    name: "bn4a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch2c"
    top: "res4a_branch2c"
    name: "scale4a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a_branch1"
    bottom: "res4a_branch2c"
    top: "res4a"
    name: "res4a"
    type: "Eltwise"
}

layer {
    bottom: "res4a"
    top: "res4a"
    name: "res4a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4a"
    top: "res4b1_branch2a"
    name: "res4b1_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2a"
    name: "bn4b1_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2a"
    name: "scale4b1_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b1_branch2a"
    bottom: "res4b1_branch2a"
    name: "res4b1_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1_branch2a"
    top: "res4b1_branch2b"
    name: "res4b1_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2b"
    name: "bn4b1_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2b"
    name: "scale4b1_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b1_branch2b"
    bottom: "res4b1_branch2b"
    name: "res4b1_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1_branch2b"
    top: "res4b1_branch2c"
    name: "res4b1_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b1_branch2c"
    top: "res4b1_branch2c"
    name: "bn4b1_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1_branch2c"
    top: "res4b1_branch2c"
    name: "scale4b1_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4a"
    bottom: "res4b1_branch2c"
    top: "res4b1"
    name: "res4b1"
    type: "Eltwise"
}

layer {
    bottom: "res4b1"
    top: "res4b1"
    name: "res4b1_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b1"
    top: "res4b2_branch2a"
    name: "res4b2_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2a"
    name: "bn4b2_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2a"
    name: "scale4b2_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b2_branch2a"
    bottom: "res4b2_branch2a"
    name: "res4b2_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2_branch2a"
    top: "res4b2_branch2b"
    name: "res4b2_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2b"
    name: "bn4b2_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2b"
    name: "scale4b2_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b2_branch2b"
    bottom: "res4b2_branch2b"
    name: "res4b2_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2_branch2b"
    top: "res4b2_branch2c"
    name: "res4b2_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b2_branch2c"
    top: "res4b2_branch2c"
    name: "bn4b2_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2_branch2c"
    top: "res4b2_branch2c"
    name: "scale4b2_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b1"
    bottom: "res4b2_branch2c"
    top: "res4b2"
    name: "res4b2"
    type: "Eltwise"
}

layer {
    bottom: "res4b2"
    top: "res4b2"
    name: "res4b2_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b2"
    top: "res4b3_branch2a"
    name: "res4b3_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2a"
    name: "bn4b3_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2a"
    name: "scale4b3_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b3_branch2a"
    bottom: "res4b3_branch2a"
    name: "res4b3_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3_branch2a"
    top: "res4b3_branch2b"
    name: "res4b3_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2b"
    name: "bn4b3_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2b"
    name: "scale4b3_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b3_branch2b"
    bottom: "res4b3_branch2b"
    name: "res4b3_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3_branch2b"
    top: "res4b3_branch2c"
    name: "res4b3_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b3_branch2c"
    top: "res4b3_branch2c"
    name: "bn4b3_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3_branch2c"
    top: "res4b3_branch2c"
    name: "scale4b3_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b2"
    bottom: "res4b3_branch2c"
    top: "res4b3"
    name: "res4b3"
    type: "Eltwise"
}

layer {
    bottom: "res4b3"
    top: "res4b3"
    name: "res4b3_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b3"
    top: "res4b4_branch2a"
    name: "res4b4_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2a"
    name: "bn4b4_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2a"
    name: "scale4b4_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b4_branch2a"
    bottom: "res4b4_branch2a"
    name: "res4b4_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4_branch2a"
    top: "res4b4_branch2b"
    name: "res4b4_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2b"
    name: "bn4b4_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2b"
    name: "scale4b4_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b4_branch2b"
    bottom: "res4b4_branch2b"
    name: "res4b4_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4_branch2b"
    top: "res4b4_branch2c"
    name: "res4b4_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b4_branch2c"
    top: "res4b4_branch2c"
    name: "bn4b4_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4_branch2c"
    top: "res4b4_branch2c"
    name: "scale4b4_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b3"
    bottom: "res4b4_branch2c"
    top: "res4b4"
    name: "res4b4"
    type: "Eltwise"
}

layer {
    bottom: "res4b4"
    top: "res4b4"
    name: "res4b4_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b4"
    top: "res4b5_branch2a"
    name: "res4b5_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2a"
    name: "bn4b5_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2a"
    name: "scale4b5_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b5_branch2a"
    bottom: "res4b5_branch2a"
    name: "res4b5_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5_branch2a"
    top: "res4b5_branch2b"
    name: "res4b5_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2b"
    name: "bn4b5_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2b"
    name: "scale4b5_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b5_branch2b"
    bottom: "res4b5_branch2b"
    name: "res4b5_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5_branch2b"
    top: "res4b5_branch2c"
    name: "res4b5_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b5_branch2c"
    top: "res4b5_branch2c"
    name: "bn4b5_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5_branch2c"
    top: "res4b5_branch2c"
    name: "scale4b5_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b4"
    bottom: "res4b5_branch2c"
    top: "res4b5"
    name: "res4b5"
    type: "Eltwise"
}

layer {
    bottom: "res4b5"
    top: "res4b5"
    name: "res4b5_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b5"
    top: "res4b6_branch2a"
    name: "res4b6_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2a"
    name: "bn4b6_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2a"
    name: "scale4b6_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b6_branch2a"
    bottom: "res4b6_branch2a"
    name: "res4b6_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6_branch2a"
    top: "res4b6_branch2b"
    name: "res4b6_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2b"
    name: "bn4b6_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2b"
    name: "scale4b6_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b6_branch2b"
    bottom: "res4b6_branch2b"
    name: "res4b6_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6_branch2b"
    top: "res4b6_branch2c"
    name: "res4b6_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b6_branch2c"
    top: "res4b6_branch2c"
    name: "bn4b6_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6_branch2c"
    top: "res4b6_branch2c"
    name: "scale4b6_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b5"
    bottom: "res4b6_branch2c"
    top: "res4b6"
    name: "res4b6"
    type: "Eltwise"
}

layer {
    bottom: "res4b6"
    top: "res4b6"
    name: "res4b6_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b6"
    top: "res4b7_branch2a"
    name: "res4b7_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2a"
    name: "bn4b7_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2a"
    name: "scale4b7_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b7_branch2a"
    bottom: "res4b7_branch2a"
    name: "res4b7_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7_branch2a"
    top: "res4b7_branch2b"
    name: "res4b7_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2b"
    name: "bn4b7_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2b"
    name: "scale4b7_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b7_branch2b"
    bottom: "res4b7_branch2b"
    name: "res4b7_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7_branch2b"
    top: "res4b7_branch2c"
    name: "res4b7_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b7_branch2c"
    top: "res4b7_branch2c"
    name: "bn4b7_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7_branch2c"
    top: "res4b7_branch2c"
    name: "scale4b7_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b6"
    bottom: "res4b7_branch2c"
    top: "res4b7"
    name: "res4b7"
    type: "Eltwise"
}

layer {
    bottom: "res4b7"
    top: "res4b7"
    name: "res4b7_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b7"
    top: "res4b8_branch2a"
    name: "res4b8_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2a"
    name: "bn4b8_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2a"
    name: "scale4b8_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b8_branch2a"
    bottom: "res4b8_branch2a"
    name: "res4b8_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8_branch2a"
    top: "res4b8_branch2b"
    name: "res4b8_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2b"
    name: "bn4b8_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2b"
    name: "scale4b8_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b8_branch2b"
    bottom: "res4b8_branch2b"
    name: "res4b8_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8_branch2b"
    top: "res4b8_branch2c"
    name: "res4b8_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b8_branch2c"
    top: "res4b8_branch2c"
    name: "bn4b8_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8_branch2c"
    top: "res4b8_branch2c"
    name: "scale4b8_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b7"
    bottom: "res4b8_branch2c"
    top: "res4b8"
    name: "res4b8"
    type: "Eltwise"
}

layer {
    bottom: "res4b8"
    top: "res4b8"
    name: "res4b8_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b8"
    top: "res4b9_branch2a"
    name: "res4b9_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2a"
    name: "bn4b9_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2a"
    name: "scale4b9_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b9_branch2a"
    bottom: "res4b9_branch2a"
    name: "res4b9_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9_branch2a"
    top: "res4b9_branch2b"
    name: "res4b9_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2b"
    name: "bn4b9_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2b"
    name: "scale4b9_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b9_branch2b"
    bottom: "res4b9_branch2b"
    name: "res4b9_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9_branch2b"
    top: "res4b9_branch2c"
    name: "res4b9_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b9_branch2c"
    top: "res4b9_branch2c"
    name: "bn4b9_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9_branch2c"
    top: "res4b9_branch2c"
    name: "scale4b9_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b8"
    bottom: "res4b9_branch2c"
    top: "res4b9"
    name: "res4b9"
    type: "Eltwise"
}

layer {
    bottom: "res4b9"
    top: "res4b9"
    name: "res4b9_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b9"
    top: "res4b10_branch2a"
    name: "res4b10_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2a"
    name: "bn4b10_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2a"
    name: "scale4b10_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b10_branch2a"
    bottom: "res4b10_branch2a"
    name: "res4b10_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10_branch2a"
    top: "res4b10_branch2b"
    name: "res4b10_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2b"
    name: "bn4b10_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2b"
    name: "scale4b10_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b10_branch2b"
    bottom: "res4b10_branch2b"
    name: "res4b10_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10_branch2b"
    top: "res4b10_branch2c"
    name: "res4b10_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b10_branch2c"
    top: "res4b10_branch2c"
    name: "bn4b10_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10_branch2c"
    top: "res4b10_branch2c"
    name: "scale4b10_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b9"
    bottom: "res4b10_branch2c"
    top: "res4b10"
    name: "res4b10"
    type: "Eltwise"
}

layer {
    bottom: "res4b10"
    top: "res4b10"
    name: "res4b10_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b10"
    top: "res4b11_branch2a"
    name: "res4b11_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2a"
    name: "bn4b11_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2a"
    name: "scale4b11_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b11_branch2a"
    bottom: "res4b11_branch2a"
    name: "res4b11_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11_branch2a"
    top: "res4b11_branch2b"
    name: "res4b11_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2b"
    name: "bn4b11_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2b"
    name: "scale4b11_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b11_branch2b"
    bottom: "res4b11_branch2b"
    name: "res4b11_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11_branch2b"
    top: "res4b11_branch2c"
    name: "res4b11_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b11_branch2c"
    top: "res4b11_branch2c"
    name: "bn4b11_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11_branch2c"
    top: "res4b11_branch2c"
    name: "scale4b11_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b10"
    bottom: "res4b11_branch2c"
    top: "res4b11"
    name: "res4b11"
    type: "Eltwise"
}

layer {
    bottom: "res4b11"
    top: "res4b11"
    name: "res4b11_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b11"
    top: "res4b12_branch2a"
    name: "res4b12_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2a"
    name: "bn4b12_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2a"
    name: "scale4b12_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b12_branch2a"
    bottom: "res4b12_branch2a"
    name: "res4b12_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12_branch2a"
    top: "res4b12_branch2b"
    name: "res4b12_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2b"
    name: "bn4b12_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2b"
    name: "scale4b12_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b12_branch2b"
    bottom: "res4b12_branch2b"
    name: "res4b12_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12_branch2b"
    top: "res4b12_branch2c"
    name: "res4b12_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b12_branch2c"
    top: "res4b12_branch2c"
    name: "bn4b12_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12_branch2c"
    top: "res4b12_branch2c"
    name: "scale4b12_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b11"
    bottom: "res4b12_branch2c"
    top: "res4b12"
    name: "res4b12"
    type: "Eltwise"
}

layer {
    bottom: "res4b12"
    top: "res4b12"
    name: "res4b12_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b12"
    top: "res4b13_branch2a"
    name: "res4b13_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2a"
    name: "bn4b13_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2a"
    name: "scale4b13_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b13_branch2a"
    bottom: "res4b13_branch2a"
    name: "res4b13_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13_branch2a"
    top: "res4b13_branch2b"
    name: "res4b13_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2b"
    name: "bn4b13_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2b"
    name: "scale4b13_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b13_branch2b"
    bottom: "res4b13_branch2b"
    name: "res4b13_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13_branch2b"
    top: "res4b13_branch2c"
    name: "res4b13_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b13_branch2c"
    top: "res4b13_branch2c"
    name: "bn4b13_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13_branch2c"
    top: "res4b13_branch2c"
    name: "scale4b13_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b12"
    bottom: "res4b13_branch2c"
    top: "res4b13"
    name: "res4b13"
    type: "Eltwise"
}

layer {
    bottom: "res4b13"
    top: "res4b13"
    name: "res4b13_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b13"
    top: "res4b14_branch2a"
    name: "res4b14_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2a"
    name: "bn4b14_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2a"
    name: "scale4b14_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b14_branch2a"
    bottom: "res4b14_branch2a"
    name: "res4b14_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14_branch2a"
    top: "res4b14_branch2b"
    name: "res4b14_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2b"
    name: "bn4b14_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2b"
    name: "scale4b14_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b14_branch2b"
    bottom: "res4b14_branch2b"
    name: "res4b14_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14_branch2b"
    top: "res4b14_branch2c"
    name: "res4b14_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b14_branch2c"
    top: "res4b14_branch2c"
    name: "bn4b14_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14_branch2c"
    top: "res4b14_branch2c"
    name: "scale4b14_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b13"
    bottom: "res4b14_branch2c"
    top: "res4b14"
    name: "res4b14"
    type: "Eltwise"
}

layer {
    bottom: "res4b14"
    top: "res4b14"
    name: "res4b14_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b14"
    top: "res4b15_branch2a"
    name: "res4b15_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2a"
    name: "bn4b15_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2a"
    name: "scale4b15_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b15_branch2a"
    bottom: "res4b15_branch2a"
    name: "res4b15_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15_branch2a"
    top: "res4b15_branch2b"
    name: "res4b15_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2b"
    name: "bn4b15_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2b"
    name: "scale4b15_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b15_branch2b"
    bottom: "res4b15_branch2b"
    name: "res4b15_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15_branch2b"
    top: "res4b15_branch2c"
    name: "res4b15_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b15_branch2c"
    top: "res4b15_branch2c"
    name: "bn4b15_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15_branch2c"
    top: "res4b15_branch2c"
    name: "scale4b15_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b14"
    bottom: "res4b15_branch2c"
    top: "res4b15"
    name: "res4b15"
    type: "Eltwise"
}

layer {
    bottom: "res4b15"
    top: "res4b15"
    name: "res4b15_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b15"
    top: "res4b16_branch2a"
    name: "res4b16_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2a"
    name: "bn4b16_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2a"
    name: "scale4b16_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b16_branch2a"
    bottom: "res4b16_branch2a"
    name: "res4b16_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16_branch2a"
    top: "res4b16_branch2b"
    name: "res4b16_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2b"
    name: "bn4b16_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2b"
    name: "scale4b16_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b16_branch2b"
    bottom: "res4b16_branch2b"
    name: "res4b16_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16_branch2b"
    top: "res4b16_branch2c"
    name: "res4b16_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b16_branch2c"
    top: "res4b16_branch2c"
    name: "bn4b16_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16_branch2c"
    top: "res4b16_branch2c"
    name: "scale4b16_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b15"
    bottom: "res4b16_branch2c"
    top: "res4b16"
    name: "res4b16"
    type: "Eltwise"
}

layer {
    bottom: "res4b16"
    top: "res4b16"
    name: "res4b16_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b16"
    top: "res4b17_branch2a"
    name: "res4b17_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2a"
    name: "bn4b17_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2a"
    name: "scale4b17_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b17_branch2a"
    bottom: "res4b17_branch2a"
    name: "res4b17_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17_branch2a"
    top: "res4b17_branch2b"
    name: "res4b17_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2b"
    name: "bn4b17_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2b"
    name: "scale4b17_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b17_branch2b"
    bottom: "res4b17_branch2b"
    name: "res4b17_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17_branch2b"
    top: "res4b17_branch2c"
    name: "res4b17_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b17_branch2c"
    top: "res4b17_branch2c"
    name: "bn4b17_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17_branch2c"
    top: "res4b17_branch2c"
    name: "scale4b17_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b16"
    bottom: "res4b17_branch2c"
    top: "res4b17"
    name: "res4b17"
    type: "Eltwise"
}

layer {
    bottom: "res4b17"
    top: "res4b17"
    name: "res4b17_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b17"
    top: "res4b18_branch2a"
    name: "res4b18_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2a"
    name: "bn4b18_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2a"
    name: "scale4b18_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b18_branch2a"
    bottom: "res4b18_branch2a"
    name: "res4b18_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18_branch2a"
    top: "res4b18_branch2b"
    name: "res4b18_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2b"
    name: "bn4b18_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2b"
    name: "scale4b18_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b18_branch2b"
    bottom: "res4b18_branch2b"
    name: "res4b18_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18_branch2b"
    top: "res4b18_branch2c"
    name: "res4b18_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b18_branch2c"
    top: "res4b18_branch2c"
    name: "bn4b18_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18_branch2c"
    top: "res4b18_branch2c"
    name: "scale4b18_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b17"
    bottom: "res4b18_branch2c"
    top: "res4b18"
    name: "res4b18"
    type: "Eltwise"
}

layer {
    bottom: "res4b18"
    top: "res4b18"
    name: "res4b18_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b18"
    top: "res4b19_branch2a"
    name: "res4b19_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2a"
    name: "bn4b19_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2a"
    name: "scale4b19_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b19_branch2a"
    bottom: "res4b19_branch2a"
    name: "res4b19_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19_branch2a"
    top: "res4b19_branch2b"
    name: "res4b19_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2b"
    name: "bn4b19_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2b"
    name: "scale4b19_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b19_branch2b"
    bottom: "res4b19_branch2b"
    name: "res4b19_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19_branch2b"
    top: "res4b19_branch2c"
    name: "res4b19_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b19_branch2c"
    top: "res4b19_branch2c"
    name: "bn4b19_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19_branch2c"
    top: "res4b19_branch2c"
    name: "scale4b19_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b18"
    bottom: "res4b19_branch2c"
    top: "res4b19"
    name: "res4b19"
    type: "Eltwise"
}

layer {
    bottom: "res4b19"
    top: "res4b19"
    name: "res4b19_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b19"
    top: "res4b20_branch2a"
    name: "res4b20_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2a"
    name: "bn4b20_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2a"
    name: "scale4b20_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b20_branch2a"
    bottom: "res4b20_branch2a"
    name: "res4b20_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20_branch2a"
    top: "res4b20_branch2b"
    name: "res4b20_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2b"
    name: "bn4b20_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2b"
    name: "scale4b20_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b20_branch2b"
    bottom: "res4b20_branch2b"
    name: "res4b20_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20_branch2b"
    top: "res4b20_branch2c"
    name: "res4b20_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b20_branch2c"
    top: "res4b20_branch2c"
    name: "bn4b20_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20_branch2c"
    top: "res4b20_branch2c"
    name: "scale4b20_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b19"
    bottom: "res4b20_branch2c"
    top: "res4b20"
    name: "res4b20"
    type: "Eltwise"
}

layer {
    bottom: "res4b20"
    top: "res4b20"
    name: "res4b20_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b20"
    top: "res4b21_branch2a"
    name: "res4b21_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2a"
    name: "bn4b21_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2a"
    name: "scale4b21_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b21_branch2a"
    bottom: "res4b21_branch2a"
    name: "res4b21_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21_branch2a"
    top: "res4b21_branch2b"
    name: "res4b21_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2b"
    name: "bn4b21_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2b"
    name: "scale4b21_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b21_branch2b"
    bottom: "res4b21_branch2b"
    name: "res4b21_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21_branch2b"
    top: "res4b21_branch2c"
    name: "res4b21_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b21_branch2c"
    top: "res4b21_branch2c"
    name: "bn4b21_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21_branch2c"
    top: "res4b21_branch2c"
    name: "scale4b21_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b20"
    bottom: "res4b21_branch2c"
    top: "res4b21"
    name: "res4b21"
    type: "Eltwise"
}

layer {
    bottom: "res4b21"
    top: "res4b21"
    name: "res4b21_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b21"
    top: "res4b22_branch2a"
    name: "res4b22_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2a"
    name: "bn4b22_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2a"
    name: "scale4b22_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b22_branch2a"
    bottom: "res4b22_branch2a"
    name: "res4b22_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22_branch2a"
    top: "res4b22_branch2b"
    name: "res4b22_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2b"
    name: "bn4b22_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2b"
    name: "scale4b22_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res4b22_branch2b"
    bottom: "res4b22_branch2b"
    name: "res4b22_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22_branch2b"
    top: "res4b22_branch2c"
    name: "res4b22_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res4b22_branch2c"
    top: "res4b22_branch2c"
    name: "bn4b22_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22_branch2c"
    top: "res4b22_branch2c"
    name: "scale4b22_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b21"
    bottom: "res4b22_branch2c"
    top: "res4b22"
    name: "res4b22"
    type: "Eltwise"
}

layer {
    bottom: "res4b22"
    top: "res4b22"
    name: "res4b22_relu"
    type: "ReLU"
}

layer {
    bottom: "res4b22"
    top: "res5a_branch1"
    name: "res5a_branch1"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "bn5a_branch1"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "scale5a_branch1"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res4b22"
    top: "res5a_branch2a"
    name: "res5a_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "bn5a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "scale5a_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5a_branch2a"
    bottom: "res5a_branch2a"
    name: "res5a_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a_branch2a"
    top: "res5a_branch2b"
    name: "res5a_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "bn5a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "scale5a_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5a_branch2b"
    bottom: "res5a_branch2b"
    name: "res5a_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a_branch2b"
    top: "res5a_branch2c"
    name: "res5a_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5a_branch2c"
    top: "res5a_branch2c"
    name: "bn5a_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch2c"
    top: "res5a_branch2c"
    name: "scale5a_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a_branch1"
    bottom: "res5a_branch2c"
    top: "res5a"
    name: "res5a"
    type: "Eltwise"
}

layer {
    bottom: "res5a"
    top: "res5a"
    name: "res5a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5a"
    top: "res5b_branch2a"
    name: "res5b_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "bn5b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "scale5b_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5b_branch2a"
    bottom: "res5b_branch2a"
    name: "res5b_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b_branch2a"
    top: "res5b_branch2b"
    name: "res5b_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "bn5b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "scale5b_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5b_branch2b"
    bottom: "res5b_branch2b"
    name: "res5b_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b_branch2b"
    top: "res5b_branch2c"
    name: "res5b_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5b_branch2c"
    top: "res5b_branch2c"
    name: "bn5b_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b_branch2c"
    top: "res5b_branch2c"
    name: "scale5b_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5a"
    bottom: "res5b_branch2c"
    top: "res5b"
    name: "res5b"
    type: "Eltwise"
}

layer {
    bottom: "res5b"
    top: "res5b"
    name: "res5b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5b"
    top: "res5c_branch2a"
    name: "res5c_branch2a"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2a"
    name: "bn5c_branch2a"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2a"
    name: "scale5c_branch2a"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5c_branch2a"
    bottom: "res5c_branch2a"
    name: "res5c_branch2a_relu"
    type: "ReLU"
}

layer {
    bottom: "res5c_branch2a"
    top: "res5c_branch2b"
    name: "res5c_branch2b"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
        pad: 2
        dilation: 2
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2b"
    name: "bn5c_branch2b"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2b"
    name: "scale5c_branch2b"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    top: "res5c_branch2b"
    bottom: "res5c_branch2b"
    name: "res5c_branch2b_relu"
    type: "ReLU"
}

layer {
    bottom: "res5c_branch2b"
    top: "res5c_branch2c"
    name: "res5c_branch2c"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        pad: 0
        stride: 1
        bias_term: false
    }
    param {
        lr_mult: 1.0
    }
}

layer {
    bottom: "res5c_branch2c"
    top: "res5c_branch2c"
    name: "bn5c_branch2c"
    type: "BatchNorm"
    batch_norm_param {
        use_global_stats: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5c_branch2c"
    top: "res5c_branch2c"
    name: "scale5c_branch2c"
    type: "Scale"
    scale_param {
        bias_term: true
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
}

layer {
    bottom: "res5b"
    bottom: "res5c_branch2c"
    top: "res5c"
    name: "res5c"
    type: "Eltwise"
}

layer {
    bottom: "res5c"
    top: "res5c"
    name: "res5c_relu"
    type: "ReLU"
}

###DSN conv 6###
layer { name: 'conv1-dsn6' type: "Convolution" bottom: 'res5c' top: 'conv1-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn6' top: 'conv1-dsn6' name: 'relu1-dsn6' type: "ReLU" }
layer { name: 'conv2-dsn6' type: "Convolution" bottom: 'conv1-dsn6' top: 'conv2-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn6' top: 'conv2-dsn6' name: 'relu2-dsn6' type: "ReLU" }
layer { name: 'conv3-dsn6' type: "Convolution" bottom: 'conv2-dsn6' top: 'conv3-dsn6'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_in_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up' bottom: 'data' top: 'upscore-dsn6' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn6" bottom: "label" top: "loss-dsn6"
  name: "loss-dsn6" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn6" bottom: "upscore-dsn6" top: "sigmoid-dsn6" include { phase: TEST} }

###DSN conv 5###
layer { name: 'conv1-dsn5' type: "Convolution" bottom: 'res4b22' top: 'conv1-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn5' top: 'conv1-dsn5' name: 'relu1-dsn5' type: "ReLU" }
layer { name: 'conv2-dsn5' type: "Convolution" bottom: 'conv1-dsn5' top: 'conv2-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn5' top: 'conv2-dsn5' name: 'relu2-dsn5' type: "ReLU" }
layer { name: 'conv3-dsn5' type: "Convolution" bottom: 'conv2-dsn5' top: 'conv3-dsn5'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_in_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top: "loss-dsn5"
  name: "loss-dsn5" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn5" bottom: "upscore-dsn5" top: "sigmoid-dsn5" include { phase: TEST} }

###DSN conv 4###
layer { name: 'conv1-dsn4' type: "Convolution" bottom: 'res3b3' top: 'conv1-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn4' top: 'conv1-dsn4' name: 'relu1-dsn4' type: "ReLU" }
layer { name: 'conv2-dsn4' type: "Convolution" bottom: 'conv1-dsn4' top: 'conv2-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn4' top: 'conv2-dsn4' name: 'relu2-dsn4' type: "ReLU" }
layer { name: 'conv3-dsn4' type: "Convolution" bottom: 'conv2-dsn4' top: 'conv3-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn6-4' }
layer { type: "Deconvolution" name: 'upsample2_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn5-4' }
layer { name: "concat-dsn4" bottom: "conv3-dsn4"  bottom: "upscore-dsn6-4" bottom: "upscore-dsn5-4" 
  top: "concat-dsn4" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn4' type: "Convolution" bottom: 'concat-dsn4' top: 'conv4-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_in_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top: "loss-dsn4"
  name: "loss-dsn4" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn4" bottom: "upscore-dsn4" top: "sigmoid-dsn4" include { phase: TEST} }

### DSN conv 3 ###
layer { name: 'conv1-dsn3' type: "Convolution" bottom: 'res2c' top: 'conv1-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn3' top: 'conv1-dsn3' name: 'relu1-dsn3' type: "ReLU" }
layer { name: 'conv2-dsn3' type: "Convolution" bottom: 'conv1-dsn3' top: 'conv2-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn3' top: 'conv2-dsn3' name: 'relu2-dsn3' type: "ReLU" }
layer { name: 'conv3-dsn3' type: "Convolution" bottom: 'conv2-dsn3' top: 'conv3-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn6-3' }
layer { type: "Deconvolution" name: 'upsample4_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn5-3' }
layer { name: "concat-dsn3" bottom: "conv3-dsn3"  bottom: "upscore-dsn6-3" bottom: "upscore-dsn5-3" 
  top: "concat-dsn3" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn3' type: "Convolution" bottom: 'concat-dsn3' top: 'conv4-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_in_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top: "loss-dsn3"
  name: "loss-dsn3" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn3" bottom: "upscore-dsn3" top: "sigmoid-dsn3" include { phase: TEST} }

### DSN conv 2 ###
layer { name: 'conv1-dsn2' type: "Convolution" bottom: 'conv1' top: 'conv1-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn2' top: 'conv1-dsn2' name: 'relu1-dsn2' type: "ReLU" }
layer { name: 'conv2-dsn2' type: "Convolution" bottom: 'conv1-dsn2' top: 'conv2-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn2' top: 'conv2-dsn2' name: 'relu2-dsn2' type: "ReLU" }
layer { name: 'conv3-dsn2' type: "Convolution" bottom: 'conv2-dsn2' top: 'conv3-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn6-2' }
layer { type: "Deconvolution" name: 'upsample8_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn5-2' }
layer { type: "Deconvolution" name: 'upsample4_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn4-2' }
layer { type: "Deconvolution" name: 'upsample2_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn3-2' }
layer { name: "concat-dsn2" bottom: "conv3-dsn2" bottom: "upscore-dsn5-2" bottom: "upscore-dsn4-2" bottom: "upscore-dsn6-2"
  bottom: "upscore-dsn3-2" top: "concat-dsn2" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn2' type: "Convolution" bottom: 'concat-dsn2' top: 'conv4-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample2_in_dsn2' bottom: 'conv4-dsn2' top: 'score-dsn2-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top: "loss-dsn2"
  name: "loss-dsn2" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn2" bottom: "upscore-dsn2" top: "sigmoid-dsn2" include { phase: TEST} }

### Concat and multiscale weight layer ###
layer { name: "concat" bottom: "upscore-dsn6"  bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" 
  bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat" concat_param { concat_dim: 1 } }
layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.2 } } }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top: "loss-fuse"
  name: "loss-fuse" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-fuse" bottom: "upscore-fuse" top: "sigmoid-fuse" include { phase: TEST } }


================================================
FILE: run_saliency.py
================================================
from __future__ import division
import numpy as np
import sys
caffe_root = '../../'
sys.path.insert(0, caffe_root + 'python')
import caffe

def upsample_filt(size):
    factor = (size + 1) // 2
    if size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = np.ogrid[:size, :size]
    return (1 - abs(og[0] - center) / factor) * \
           (1 - abs(og[1] - center) / factor)

# set parameters s.t. deconvolutional layers compute bilinear interpolation
# N.B. this is for deconvolution without groups
def interp_surgery(net, layers):
    for l in layers:
        m, k, h, w = net.params[l][0].data.shape
        if m != k:
            print 'input + output channels need to be the same'
            raise
        if h != w:
            print 'filters need to be square'
            raise
        filt = upsample_filt(h)
        net.params[l][0].data[range(m), range(k), :, :] = filt

base_weights = 'vgg16.caffemodel'  # the vgg16 model

# init
caffe.set_mode_gpu()
caffe.set_device(0)

solver = caffe.SGDSolver('solver.prototxt')

# do net surgery to set the deconvolution weights for bilinear interpolation
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
interp_surgery(solver.net, interp_layers)

# copy base weights for fine-tuning
#solver.restore('./snapshot/ours_iter_4000.solverstate')
solver.net.copy_from(base_weights)

solver.step(12000)


================================================
FILE: solver.prototxt
================================================
train_net: "train_val.prototxt"
# lr for fine-tuning should be lower than when starting from scratch
#debug_info: true
base_lr: 1e-8
lr_policy: "step"
#power: 0.9
gamma: 0.1
iter_size: 10
# stepsize should also be lower, as we're closer to being done
stepsize: 7500
average_loss: 20
display: 20
max_iter: 12000
momentum: 0.90
weight_decay: 0.0005
snapshot: 1000
snapshot_prefix: "snapshot/ours"
# uncomment the following to default to CPU mode solving
# solver_mode: CPU 


================================================
FILE: train_val.prototxt
================================================
### DSS ###
name: "DSS"
layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include { phase: TRAIN }
  transform_param {
    mirror: true
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  image_data_param {
    root_folder: "/opt/dataset/saliency/msra_b/"
    source: "../../data/msra_b/train.lst"
    batch_size: 1
    shuffle: true
    normalize: true
  }
}

layer { bottom: 'data' top: 'conv1_1' name: 'conv1_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 5 kernel_size: 3 } }
layer { bottom: 'conv1_1' top: 'conv1_1' name: 'relu1_1' type: "ReLU" }
layer { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: "ReLU" }
layer { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: "ReLU" }
layer { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: "ReLU" }
layer { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: "ReLU" }
layer { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: "ReLU" }
layer { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: "ReLU" }
layer { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: "ReLU" }
layer { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: "ReLU" }
layer { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: "ReLU" }
layer { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } }

layer { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: "ReLU" }
layer { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: "ReLU" }
layer { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: "ReLU" }
layer { bottom: 'conv5_3' top: 'pool5' name: 'pool5' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } }
layer { bottom: 'pool5' top: 'pool5a' name: 'pool5a' type: "Pooling"
  pooling_param { pool: AVE kernel_size: 3 stride: 1 pad: 1 } }

###DSN conv 6###
layer { name: 'conv1-dsn6' type: "Convolution" bottom: 'pool5a' top: 'conv1-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn6' top: 'conv1-dsn6' name: 'relu1-dsn6' type: "ReLU" }
layer { name: 'conv2-dsn6' type: "Convolution" bottom: 'conv1-dsn6' top: 'conv2-dsn6'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 3 kernel_size: 7 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn6' top: 'conv2-dsn6' name: 'relu2-dsn6' type: "ReLU" }
layer { name: 'conv3-dsn6' type: "Convolution" bottom: 'conv2-dsn6' top: 'conv3-dsn6'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_in_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up' bottom: 'data' top: 'upscore-dsn6' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn6" bottom: "label" top: "loss-dsn6"
  name: "loss-dsn6" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn6" bottom: "upscore-dsn6" top: "sigmoid-dsn6" include { phase: TEST} }

###DSN conv 5###
layer { name: 'conv1-dsn5' type: "Convolution" bottom: 'conv5_3' top: 'conv1-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn5' top: 'conv1-dsn5' name: 'relu1-dsn5' type: "ReLU" }
layer { name: 'conv2-dsn5' type: "Convolution" bottom: 'conv1-dsn5' top: 'conv2-dsn5'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 2 kernel_size: 5
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn5' top: 'conv2-dsn5' name: 'relu2-dsn5' type: "ReLU" }
layer { name: 'conv3-dsn5' type: "Convolution" bottom: 'conv2-dsn5' top: 'conv3-dsn5'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_in_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top: "loss-dsn5"
  name: "loss-dsn5" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn5" bottom: "upscore-dsn5" top: "sigmoid-dsn5" include { phase: TEST} }

###DSN conv 4###
layer { name: 'conv1-dsn4' type: "Convolution" bottom: 'conv4_3' top: 'conv1-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn4' top: 'conv1-dsn4' name: 'relu1-dsn4' type: "ReLU" }
layer { name: 'conv2-dsn4' type: "Convolution" bottom: 'conv1-dsn4' top: 'conv2-dsn4'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn4' top: 'conv2-dsn4' name: 'relu2-dsn4' type: "ReLU" }
layer { name: 'conv3-dsn4' type: "Convolution" bottom: 'conv2-dsn4' top: 'conv3-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn6-4' }
layer { type: "Deconvolution" name: 'upsample2_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-4'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-4' bottom: 'conv3-dsn4' top: 'upscore-dsn5-4' }
layer { name: "concat-dsn4" bottom: "conv3-dsn4"  bottom: "upscore-dsn6-4" bottom: "upscore-dsn5-4" 
  top: "concat-dsn4" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn4' type: "Convolution" bottom: 'concat-dsn4' top: 'conv4-dsn4'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_in_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top: "loss-dsn4"
  name: "loss-dsn4" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn4" bottom: "upscore-dsn4" top: "sigmoid-dsn4" include { phase: TEST} }

### DSN conv 3 ###
layer { name: 'conv1-dsn3' type: "Convolution" bottom: 'conv3_3' top: 'conv1-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn3' top: 'conv1-dsn3' name: 'relu1-dsn3' type: "ReLU" }
layer { name: 'conv2-dsn3' type: "Convolution" bottom: 'conv1-dsn3' top: 'conv2-dsn3'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 2 kernel_size: 5 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn3' top: 'conv2-dsn3' name: 'relu2-dsn3' type: "ReLU" }
layer { name: 'conv3-dsn3' type: "Convolution" bottom: 'conv2-dsn3' top: 'conv3-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample8_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn6-3' }
layer { type: "Deconvolution" name: 'upsample4_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-3'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-3' bottom: 'conv3-dsn3' top: 'upscore-dsn5-3' }
layer { name: "concat-dsn3" bottom: "conv3-dsn3"  bottom: "upscore-dsn6-3" bottom: "upscore-dsn5-3" 
  top: "concat-dsn3" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn3' type: "Convolution" bottom: 'concat-dsn3' top: 'conv4-dsn3'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample4_in_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top: "loss-dsn3"
  name: "loss-dsn3" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn3" bottom: "upscore-dsn3" top: "sigmoid-dsn3" include { phase: TEST} }

### DSN conv 2 ###
layer { name: 'conv1-dsn2' type: "Convolution" bottom: 'conv2_2' top: 'conv1-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn2' top: 'conv1-dsn2' name: 'relu1-dsn2' type: "ReLU" }
layer { name: 'conv2-dsn2' type: "Convolution" bottom: 'conv1-dsn2' top: 'conv2-dsn2'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn2' top: 'conv2-dsn2' name: 'relu2-dsn2' type: "ReLU" }
layer { name: 'conv3-dsn2' type: "Convolution" bottom: 'conv2-dsn2' top: 'conv3-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample16_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn6-2' }
layer { type: "Deconvolution" name: 'upsample8_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn5-2' }
layer { type: "Deconvolution" name: 'upsample4_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn4-2' }
layer { type: "Deconvolution" name: 'upsample2_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-2'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-2' bottom: 'conv3-dsn2' top: 'upscore-dsn3-2' }
layer { name: "concat-dsn2" bottom: "conv3-dsn2" bottom: "upscore-dsn5-2" bottom: "upscore-dsn4-2" bottom: "upscore-dsn6-2"
  bottom: "upscore-dsn3-2" top: "concat-dsn2" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn2' type: "Convolution" bottom: 'concat-dsn2' top: 'conv4-dsn2'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 }
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample2_in_dsn2' bottom: 'conv4-dsn2' top: 'score-dsn2-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top: "loss-dsn2"
  name: "loss-dsn2" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn2" bottom: "upscore-dsn2" top: "sigmoid-dsn2" include { phase: TEST} }

## DSN conv 1 ###
layer { name: 'conv1-dsn1' type: "Convolution" bottom: 'conv1_2' top: 'conv1-dsn1'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv1-dsn1' top: 'conv1-dsn1' name: 'relu1-dsn1' type: "ReLU" }
layer { name: 'conv2-dsn1' type: "Convolution" bottom: 'conv1-dsn1' top: 'conv2-dsn1'
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: 'conv2-dsn1' top: 'conv2-dsn1' name: 'relu2-dsn1' type: "ReLU" }
layer { name: 'conv3-dsn1' type: "Convolution" bottom: 'conv2-dsn1' top: 'conv3-dsn1'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Deconvolution" name: 'upsample32_dsn6' bottom: 'conv3-dsn6' top: 'score-dsn6-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 64 stride: 32 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn6-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn6-1' }
layer { type: "Deconvolution" name: 'upsample16_dsn5' bottom: 'conv3-dsn5' top: 'score-dsn5-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn5-1' }
layer { type: "Deconvolution" name: 'upsample8_dsn4' bottom: 'conv4-dsn4' top: 'score-dsn4-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn4-1' }
layer { type: "Deconvolution" name: 'upsample4_dsn3' bottom: 'conv4-dsn3' top: 'score-dsn3-up-1'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up-1' bottom: 'conv3-dsn1' top: 'upscore-dsn3-1' }
layer { name: "concat-dsn1" bottom: "conv3-dsn1"  bottom: "upscore-dsn5-1" bottom: "upscore-dsn4-1" bottom: "upscore-dsn6-1"
  bottom: "upscore-dsn3-1" top: "concat-dsn1" type: "Concat" concat_param { concat_dim: 1} }
layer { name: 'conv4-dsn1' type: "Convolution" bottom: 'concat-dsn1' top: 'score-dsn1-up'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1
  weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn1-up' bottom: 'data' top: 'upscore-dsn1' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn1" bottom: "label" top: "loss-dsn1"
  name: "loss-dsn1" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-dsn1" bottom: "upscore-dsn1" top: "sigmoid-dsn1" include { phase: TEST } }

### Concat and multiscale weight layer ###
layer { name: "concat" bottom: "upscore-dsn1"  bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" 
  bottom: "upscore-dsn5" bottom: "upscore-dsn6" top: "concat-upscore" type: "Concat" concat_param { concat_dim: 1 } }
layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.1667 } } }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top: "loss-fuse"
  name: "loss-fuse" include { phase: TRAIN } loss_weight: 1 }
layer { type: "Sigmoid" name: "sigmoid-fuse" bottom: "upscore-fuse" top: "sigmoid-fuse" include { phase: TEST } }