Full Code of Andrew-Qibin/DSS for AI

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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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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
   },
   "outputs": [],
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
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
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