TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

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Deep Learning TransFGU
Overview

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, Rong Jin

[Preprint]

Getting Started

Create the environment

# create conda env
conda create -n TransFGU python=3.8
# activate conda env
conda activate TransFGU
# install pytorch
conda install pytorch=1.8 torchvision cudatoolkit=10.1
# install other dependencies
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html
pip install -r requirements.txt

Dataset Preparation

the structure of dataset folders should be as follow:

data/
    │── MSCOCO/
    │     ├── images/
    │     │     ├── train2017/
    │     │     └── val2017/
    │     └── annotations/
    │           ├── train2017/
    │           ├── val2017/
    │           ├── instances_train2017.json
    │           └── instances_val2017.json
    │── Cityscapes/
    │     ├── leftImg8bit/
    │     │     ├── train/
    │     │     │       ├── aachen
    │     │     │       └── ...
    │     │     └──── val/
    │     │             ├── frankfurt
    │     │             └── ...
    │     └── gtFine/
    │           ├── train/
    │           │       ├── aachen
    │           │       └── ...
    │           └──── val/
    │                   ├── frankfurt
    │                   └── ...
    │── PascalVOC/
    │     ├── JPEGImages/
    │     ├── SegmentationClass/
    │     └── ImageSets/
    │           └── Segmentation/
    │                   ├── train.txt
    │                   └── val.txt
    └── LIP/
          ├── train_images/
          ├── train_segmentations/
          ├── val_images/
          ├── val_segmentations/
          ├── train_id.txt
          └── val_id.txt

Model download

Name mIoU Pixel Accuracy Model
COCOStuff-27 16.19 44.52 Google Drive
COCOStuff-171 11.93 34.32 Google Drive
COCO-80 12.69 64.31 Google Drive
Cityscapes 16.83 77.92 Google Drive
Pascal-VOC 37.15 83.59 Google Drive
LIP-5 25.16 65.76 Google Drive
LIP-16 15.49 60.08 Google Drive
LIP-19 12.24 42.52 Google Drive

Train and Evaluate Our Method

To train and evaluate our method on different datasets under desired granularity level, please follow the instructions here.

Citation

If you find our work useful in your research, please consider citing:

@article{yin2021transfgu,
  title={TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation},
  author={Zhaoyun, Yin and Pichao, Wang and Fan, Wang and Xianzhe, Xu and Hanling, Zhang and Hao, Li and Rong, Jin},
  journal={arXiv preprint arXiv:2112.01515},
  year={2021}
}

LICENSE

The code is released under the MIT license.

Copyright

Copyright (C) 2010-2021 Alibaba Group Holding Limited.

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Comments
  • hi，about token file problem

    hi，about token file problem

    ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists. I have a question for you，The error is to obtain token by CUDA_VISIBLE_DEVICES=0 python pascalvoc_crop.py，The generated token file is in the trained directory

    opened by yangkunhub 10
  • Please provide the specific version in requirement

    Please provide the specific version in requirement

    Thanks for the innovation work. We meet some problem when installing and train/val, e.g. scared problem, and conflict when installing kornia (0.5.8 seems OK). Could you please provide the specific version in requirement.

    opened by Nonmy 2
  • Downloads of Cityscapes Dataset

    Downloads of Cityscapes Dataset

    Hi, thanks for the interesting work!

    Could you please check/update the download links of Cityscapes Dataset at this project page? Many thanks!

    Best, Jun-Pu Zhang

    opened by ZHANG-Jun-Pu 2
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