The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

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Deep Learning FAPIS
Overview

FAPIS

The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

drawing

Introduction

This repo is primarily based on the Pytorch implementation of Siamese Mask-RCNN and we use mmdetection toolbox to finish it.

The official code of Siamese Mask-RCNN can be found in siamese mask-rcnn

Installation

Please follow the installation in README_mmdetection.md, to compile the necessary libraries, please read the compile.sh file

Prepare COCO dataset

ln -s $path/to/coco data/coco

Training

Please read train_FAPISv2.sh for some sample commands

Testing

Please read test_FAPISv2.sh for some sample commands

and run run.sh the results will be saved in results.txt file

Visualize the results

python tools/test.py configs/FAPISv2_fcos_r50_caffe_fpn_gn_1x_4gpu.py work_dirs/FAPISv2_fcos_use_rf_mask_constrain_parts_unet_dist_part_0/latest.pth --show

Citation

Our paper:

@inproceedings{nguyen2021fapis,
  title={FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter},
  author={Nguyen, Khoi and Todorovic, Sinisa},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11099--11108},
  year={2021}
}

The Siamese Mask-RCNN paper:

@article{michaelis_one-shot_2018,
    title = {One-Shot Instance Segmentation},
    author = {Michaelis, Claudio and Ustyuzhaninov, Ivan and Bethge, Matthias and Ecker, Alexander S.},
    year = {2018},
    journal = {arXiv},
    url = {http://arxiv.org/abs/1811.11507}
}

This project is based on mmdetection toolbox.

@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li,
             Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng,
             Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu,
             Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin},
  journal = {arXiv preprint arXiv:1906.07155},
  year    = {2019}
}

Thanks for their contributions

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Comments
  • Open mmlab pretrain model urls fail

    Open mmlab pretrain model urls fail

    If you're using older versions of mmcv, the URLs to mmlab's pretrain models are hard code in the your_path/mmcv/runner/checkpoint.py. Those URLs are no longer supported. To download the pretrain models, you need to replace https://s3.ap-northeast-2.amazonaws.com/open-mmlab with https://download.openmmlab.com. See here for more details about this issue.

    opened by EverleyTseng 0
  • No pretrained models from authors?

    No pretrained models from authors?

    No clear instructions where the pretrained FAPIS models are located. Also, all the README files put in the main directory, which seems not clear enough for a reader about old and novel code and instructions.

    opened by tooHotSpot 0
  • FileNotFoundError: [Errno 2] No such file or directory: 'data/NMF_16_1000.pth'

    FileNotFoundError: [Errno 2] No such file or directory: 'data/NMF_16_1000.pth'

    Hi~Thanks for the fantastic work. I am very interested but I came across a problom when running the training script. I don't know where I can find NMF_16_1000.pth?

    opened by hanyue1648 0
Owner
Khoi Nguyen
Khoi Nguyen
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