A Simple Pooling-Based Design for Real-Time Salient Object Detection

This is a PyTorch implementation of our CVPR 2019 paper.

docker build -t poolnet .

Dev

docker run --gpus all --shm-size 8G -p 6000:5000 --rm -it -v $(pwd):/app --name poolnet poolnet bash

Prod

docker run --gpus all --shm-size 8G -p 6000:5000 --detach --name poolnet --rm poolnet

Prerequisites

• Pytorch 0.4.1+
• torchvision

Update

1. We released our code for joint training with edge, which is also our best performance model.
2. You may refer to this repo for results evaluation: SalMetric.

Usage

1. Clone the repository

git clone https://github.com/backseason/PoolNet.git
cd PoolNet/

2. Download the datasets

Download the following datasets and unzip them into data folder.

• MSRA-B and HKU-IS dataset. The .lst file for training is data/msrab_hkuis/msrab_hkuis_train_no_small.lst.
• DUTS dataset. The .lst file for training is data/DUTS/DUTS-TR/train_pair.lst.
• BSDS-PASCAL dataset. The .lst file for training is ./data/HED-BSDS_PASCAL/bsds_pascal_train_pair_r_val_r_small.lst.
• Datasets for testing.

3. Download the pre-trained models for backbone

Download the following pre-trained models GoogleDrive | BaiduYun (pwd: 27p5) into dataset/pretrained folder.

4. Train

1. Set the --train_root and --train_list path in train.sh correctly.

2. We demo using ResNet-50 as network backbone and train with a initial lr of 5e-5 for 24 epoches, which is divided by 10 after 15 epochs.

./train.sh

3. We demo joint training with edge using ResNet-50 as network backbone and train with a initial lr of 5e-5 for 11 epoches, which is divided by 10 after 8 epochs. Each epoch runs for 30000 iters.

./joint_train.sh

4. After training the result model will be stored under results/run-* folder.

5. Test

For single dataset testing: * changes accordingly and --sal_mode indicates different datasets (details can be found in main.py)

python main.py --mode='test' --model='results/run-*/models/final.pth' --test_fold='results/run-*-sal-e' --sal_mode='e'

For all datasets testing used in our paper: 2 indicates the gpu to use

./forward.sh 2 main.py results/run-*

For joint training, to get salient object detection results use

./forward.sh 2 joint_main.py results/run-*

to get edge detection results use

./forward_edge.sh 2 joint_main.py results/run-*

All results saliency maps will be stored under results/run-*-sal-* folders in .png formats.

6. Pre-trained models, pre-computed results and evaluation results

We provide the pre-trained model, pre-computed saliency maps and evaluation results for:

1. PoolNet-ResNet50 w/o edge model GoogleDrive | BaiduYun (pwd: 2uln).
2. PoolNet-ResNet50 w/ edge model (best performance) GoogleDrive | BaiduYun (pwd: ksii).
3. PoolNet-VGG16 w/ edge model (pre-computed maps) GoogleDrive | BaiduYun (pwd: 3wgc).

Note：

1. only support bath_size=1
2. Except for the backbone we do not use BN layer.

7. Contact

If you have any questions, feel free to contact me via: j04.liu(at)gmail.com.

If you think this work is helpful, please cite

@inproceedings{Liu2019PoolSal,
  title={A Simple Pooling-Based Design for Real-Time Salient Object Detection},
  author={Jiang-Jiang Liu and Qibin Hou and Ming-Ming Cheng and Jiashi Feng and Jianmin Jiang},
  booktitle={IEEE CVPR},
  year={2019},
}

Thanks to DSS and DSS-pytorch.