Awesome

DSS-PyTorch

PyTorch implement of Deeply Supervised Salient Object Detection with Short Connection

The official caffe version: DSS

Prerequisites

Python 3
Pytorch 0.4.1+
torchvision
visdom （optional for visualization）
PyDenseCRF（optional for CRF post-process）

Results

The information of Loss:

Example output:

Note: here the "blur boundary" caused by bad combine method

Different connection output：

Some difference

The original paper use：$Z=h(\sum_{i=2}^4 f_mR^{(m)})$，here we use $Z=h(\sum_{i=1}^6 f_mR^{(m)})$ in inference stage

Results Reproduct

Dataset (MSRA-B)	Paper	Here (v1)	Only Fusion (v1)	Here (v2)	Only Fusion (v2)	Here(v2 700)
MAE (without CRF)	0.043	0.054	0.052	0.068	0.052	0.051
F_beta (without CRF)	0.920	0.910	0.914	0.912	0.910	0.918
MAE (with CRF)	0.028	0.047	0.048	0.047	0.049	0.047
F_beta (with CRF)	0.927	0.916	0.917	0.915	0.918	0.923

Note：

v1 means use average fusion , v2 means use learnable fusion
You can try to use other "inference stragedy"（I think other combine can get better results --- here use sout-2+sout-3+sout-4+fusion --- you can just change self.select）
v2 700 means training with 700 epochs. （I use pre-trained model by 500 epochs：so the optimizer is a little differnt to direct 700 eopch）

Usage

1. Clone the repository

git clone git@github.com:AceCoooool/DSS-pytorch.git
cd DSS-pytorch/

2. Download the dataset

Download the MSRA-B dataset. （If you can not find this dataset, email to me --- I am not sure whether it's legal to put it on BaiDuYun）

# file construction
MSRA-B
  --- annotation
      --- xxx.png
      --- xxx.png
  --- image
      --- xxx.jpg
      --- xxx.jpg
  --- test_cvpr2013.txt
  --- train_cvpr2013.txt
  --- valid_cvpr2013.txt
  --- test_cvpr2013_debug.txt
  --- train_cvpr2013_debug.txt
  --- valid_cvpr2013_debug.txt

3. Get pre-trained vgg

cd tools/
python extract_vgg.py
cd ..

4. Demo

pleease see demo.ipynb

Note:

default choose: download and copy the pretrained model to weights directory

5. Train

python main.py --mode='train' --train_path='you_data' --label_path='you_label' --batch_size=8 --visdom=True --train_file='you_file'

Note:

--val=True add the validation (but your need to add the --val_path, --val_file and --val_label)
you_data, you_label means your training data root. (connect to the step 2)
If you Download the data to youhome/data/MSRA-B（you can not "implicity" the path）

6. Test

python main.py --mode='test' --test_path='you_data' --test_label='your_label' --use_crf=False --model='your_trained_model' --test_file='you_file'

Note：

only support bath_size=1
--use_crf=True：means use CRF post-process