Awesome

PyTorch implementation for Pyramid Feature Attention Network for Saliency Detection, CVPR 2019

Install Dependencies

The code is written in Python 3.6 using the following libraries:

numpy
tqdm
opencv-python
torch==1.1.0
torchvision==0.3.0

Install the libraries using requirements.txt as:

pip install -r requirements.txt

Data

For training, download the DUTS dataset into the data directory. Follow the folder structure given below. To train the model on any other dataset, modify the path to the input images and GT saliency maps for training and testing in dataloader.py.

Folder Structure

While training, the models are saved in a folder specifying the hyper-parameters for that run under the models directory . The directory structure looks like this:

├── data
│   └── DUTS
│       ├── DUTS-TE
│       │   ├── DUTS-TE-Image
│       │   │   ├── ILSVRC2012_test_00000003.jpg
│       │   │   ├── ILSVRC2012_test_00000023.jpg
│       │   │   └── *.jpg
│       │   └── DUTS-TE-Mask
│       │       ├── ILSVRC2012_test_00000003.png
│       │       ├── ILSVRC2012_test_00000023.png
│       │       └── *.png
│       └── DUTS-TR
│           ├── DUTS-TR-Image
│           │   ├── ILSVRC2012_test_00000004.jpg
│           │   ├── ILSVRC2012_test_00000018.jpg
│           │   └── *.jpg
│           └── DUTS-TR-Mask
│               ├── ILSVRC2012_test_00000004.png
│               ├── ILSVRC2012_test_00000018.png
│               └── *.png
├── images
│   └── saliency_results.jpg
├── inference.py
├── LICENSE
├── models
│   └── alph-0.7_wbce_w0-1.0_w1-1.15
│       └── weights
│           └── best-model_*.pth
│       └── optimizers
│           └── best-opt_*.pth
├── README.md
├── requirements.txt
├── src
│   ├── attention.py
│   ├── dataloader.py
│   ├── loss.py
│   ├── model.py
│   └── utils.py
├── train.py
└── Zhao_Pyramid_Feature_Attention_Network_for_Saliency_Detection_CVPR_2019_paper.pdf

Pre-trained Model

Download the pre-trained model from Google Drive.

Usage

Training:

Use the command below for training, modify the run-time arguments (like hyper-parameters for training, path to save the models, etc.) as required:

python train.py

Inference:

Use the command below for inference, modify the run-time arguments (like path to the pre-trained model, path to the folder containing images, etc.) as required:

python inference.py

Results

Some qualitative results from the model are shown below:

Reference

Keras implementation : LINK