PFENet

This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

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.

[Update] We have uploaded the lists we use in our paper.

  • The train/val lists for COCO contain 82081 and 40137 images respectively. They are the default train/val splits of COCO.
  • The train/val lists for PASCAL5i contain 5953 and 1449 images respectively. The train list should be voc_sbd_merge_noduplicate.txt and the val list is the original val list of pascal voc (val.txt).

To get voc_sbd_merge_noduplicate.txt:

  • We first merge the original VOC (voc_original_train.txt) and SBD (sbd_data.txt) training data.
  • [Important] sbd_data.txt does not overlap with the PASCALVOC 2012 validation data.
  • The merged list (voc_sbd_merge.txt) is then processed by the script (duplicate_removal.py) to remove the duplicate images and labels.

Run Demo / Test with Pretrained Models

  • Please download the pretrained models.

  • We provide 8 pre-trained models: 4 ResNet-50 based models for PASCAL-5i and 4 VGG-16 based models for COCO.

  • Update the config file by speficifying the target split and path (weights) for loading the checkpoint.

  • 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 a very early version of SemSeg: https://github.com/hszhao/semseg.

Other projects in few-shot segmentation:

Many thanks to their greak work!

Citation

If you find this project useful, please consider citing:

@article{tian2020pfenet,
  title={Prior Guided Feature Enrichment Network for Few-Shot Segmentation},
  author={Tian, Zhuotao and Zhao, Hengshuang and Shu, Michelle and Yang, Zhicheng and Li, Ruiyu and Jia, Jiaya},
  journal={TPAMI},
  year={2020}
}

GitHub

GitHub - dvlab-research/PFENet: PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).
PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI). - GitHub - dvlab-research/PFENet: PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).
GitHubdvlab-research