PyTorch implement of Deeply Supervised Salient Object Detection with Short Connection
The official caffe version: DSS
- Python 3
- Pytorch 0.4.1+
- torchvision
- visdom (optional for visualization)
- PyDenseCRF(optional for CRF post-process)
The information of Loss:
Example output:
Note: here the "blur boundary" caused by bad combine method
Different connection output:
- 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
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)
git clone [email protected]:AceCoooool/DSS-pytorch.git
cd DSS-pytorch/
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
cd tools/
python extract_vgg.py
cd ..
pleease see demo.ipynb
Note:
- default choose: download and copy the pretrained model to
weights
directory
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)
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# DSS-pytorch