Part-aware-Prototype-Network
Official Implementation of Part-aware Prototype Network for Few-shot Semantic Segmentation (ECCV 2020)
Installation
Dependencies
Get Started
git clone
cd PPNet-PyTorch
mkdir logs
mkdir outputs
Data Preparation for VOC Dataset
1. Download Pascal VOC dataset
Please go to PANet and download VOC dataset and put them under FewShotSeg-dataset/Pascal folder.
# symlink the pascal dataset
mkdir -p FewShotSeg-dataset/Pascal
ln -s /path_to_pascal_dataset/ FewShotSeg-dataset/Pascal/
2. Download pretrained model
Download the ResNet50 and Resnet101 weights and put them under FewShotSeg-dataset/cache/ folder.
3. Download the unlabel superpixel data
Download the unlabel superpixel from here and put it under FewShotSeg-dataset/Pascal/superpixel folder. If you want to generate your own superpixel data, please follow the fast_slic.
Training & Evaluation in Command Line for Pascal VOC
# Train baseline model
sh script/train_fewshot.sh
# Train part model
sh script/train_part.sh
# Train part+semantic branch model
sh script/train_part_sem.sh
# Train part + semantic branch + unlabel data model
sh script/train_graph.sh
Inference by pretrained model
Change the ckpt_dir in script to your pretrained model path.
# Test baseline model
sh script/test_fewshot.sh
# Test part model
sh script/test_part.sh
# Test part+semantic branch model
sh script/test_part_sem.sh
# Test part + semantic branch + unlabel data model
sh script/test_graph.sh
1-way 1-shot
| 1-way 1-shot | Download link | meanIoU |
|---|---|---|
| PANet* | model | 49.10 |
| + PAP | model | 50.40 |
| + PAP + SEM | model | 51.50 |
| + PAP + SEM + UD | model | 52.84 |
log files are available here
N-way K-shot final model
| N-way K-shot | Setting | Download link | meanIoU |
|---|---|---|---|
| 1-way 5-shot | + PAP + SEM + UD | model | 62.97 |
| 2-way 1-shot | + PAP + SEM + UD | model | 51.65 |
| 2-way 5-shot | + PAP + SEM + UD | model | 61.30 |
Visualization for Pascal VOC
Citation
Please consider citing our paper if the project helps your research. BibTeX reference is as follows.
@inproceedings{liu2020part,
title={Part-aware Prototype Network for Few-shot Semantic Segmentation},
author={Liu, Yongfei and Zhang, Xiangyi and Zhang, Songyang and He, Xuming},
booktitle={European Conference on Computer Vision},
pages={142--158},
year={2020},
organization={Springer}
}
References
Part of our code are based on PANet