PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

This repo contains code for our ICCV 2019 paper PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment.

Dependencies

• Python 3.6 +
• PyTorch 1.0.1
• torchvision 0.2.1
• NumPy, SciPy, PIL
• pycocotools
• sacred 0.7.5
• tqdm 4.32.2

Data Preparation for VOC Dataset

1. Download

   SegmentationClassAug

   ,

   SegmentationObjectAug

   ,

   ScribbleAugAuto

   from here and put them under

   VOCdevkit/VOC2012

   .

2. Download

   Segmentation

   from here and use it to replace

   VOCdevkit/VOC2012/ImageSets/Segmentation

   .

Usage

1. Download the ImageNet-pretrained weights of VGG16 network from

   torchvision

   : https://download.pytorch.org/models/vgg16-397923af.pth and put it under

   PANet/pretrained_model

   folder.

2. Change configuration via

   config.py

   , then train the model using

   python train.py

   or test the model using

   python test.py

   . You can use

   sacred

   features, e.g.

   python train.py with gpu_id=2

   .

Citation

Please consider citing our paper if the project helps your research. BibTeX reference is as follows.

@InProceedings{Wang_2019_ICCV,
author = {Wang, Kaixin and Liew, Jun Hao and Zou, Yingtian and Zhou, Daquan and Feng, Jiashi},
title = {PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}