GQNet

This is the implementation of our paper Few-Shot Segmentation with Global and Local Contrastive Learning.

Get Started

Environment

• torch==1.4.0 (torch version >= 1.0.1.post2 should be okay to run this repo)
• numpy==1.18.4
• tensorboardX==1.8
• cv2==4.2.0

Datasets and Data Preparation

Please download the following datasets:

• PASCAL-5i is based on the PASCAL VOC 2012 and SBD where the val images should be excluded from the list of training samples.

• COCO 2014.

This code reads data from .txt files where each line contains the paths for image and the correcponding label respectively. Image and label paths are seperated by a space. Example is as follows:

image_path_1 label_path_1
image_path_2 label_path_2
image_path_3 label_path_3
...
image_path_n label_path_n

Then update the train/val/test list paths in the config files.

Run Demo / Test with Pretrained Models

• Please download the pretrained models.

• We provide 8 pre-trained models: 4 ResNet-50 based [models] for COCO.

• Execute mkdir initmodel at the root directory.

• Download the ImageNet pretrained backbones and put them into the initmodel directory.

• Then execute the command:

  sh test.sh {*dataset*} {*model_config*}

Example: Test PFENet with ResNet50 on the split 0 of PASCAL-5i:

sh test.sh pascal split0_resnet50

Train

Execute this command at the root directory:

sh train.sh {*dataset*} {*model_config*}

Related Repositories

This project is built upon PFENet. Many thanks to their greak work!

Citation

If you find this project useful, please consider citing:

@article{liu2021few,
  title={Few-shot segmentation with global and local contrastive learning},
  author={Liu, Weide and Wu, Zhonghua and Ding, Henghui and Liu, Fayao and Lin, Jie and Lin, Guosheng},
  journal={arXiv preprint arXiv:2108.05293},
  year={2021}
}