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SeMask Mask2Former

This repo contains the code for our paper SeMask: Semantically Masked Transformers for Semantic Segmentation. It is based on Mask2Former.

Contents

  1. Results
  2. Setup Instructions
  3. Citing SeMask

1. Results

  • † denotes the backbones were pretrained on ImageNet-22k and 384x384 resolution images.
  • Pre-trained models can be downloaded following the instructions given under tools.

ADE20K

Method Backbone Crop Size mIoU mIoU (ms+flip) #params config Checkpoint
SeMask-L Mask2Former SeMask Swin-L 640x640 56.41 57.52 222M config checkpoint

Cityscapes

Method Backbone Crop Size mIoU mIoU (ms+flip) #params config Checkpoint
SeMask-L Mask2Former SeMask Swin-L 512x1024 83.97 84.98 222M config checkpoint

2. Setup Instructions

Installation

  • We developed the codebase using Pytorch v1.9.0 and python 3.8.
    pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
    
  • See installation instructions.

Getting Started

See Preparing Datasets for Mask2Former.

See Getting Started with Mask2Former.

3. Citing SeMask

@article{jain2021semask,
  title={SeMask: Semantically Masking Transformer Backbones for Effective Semantic Segmentation},
  author={Jitesh Jain and Anukriti Singh and Nikita Orlov and Zilong Huang and Jiachen Li and Steven Walton and Humphrey Shi},
  journal={arXiv},
  year={2021}
}