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SHI-Labs / OneFormer

  • пятница, 18 ноября 2022 г. в 00:34:19
https://github.com/SHI-Labs/OneFormer


[Preprint] OneFormer: One Transformer to Rule Universal Image Segmentation, 2022



OneFormer: One Transformer to Rule Universal Image Segmentation

Framework: PyTorch Open In Colab License

PWC PWC PWC PWC PWC PWC PWC PWC PWC

Jitesh Jain, Jiachen Li†, MangTik Chiu†, Ali Hassani, Nikita Orlov, Humphrey Shi

† Equal Contribution

[Project Page] [arXiv] [pdf] [BibTeX]

This repo contains the code for our paper OneFormer: One Transformer to Rule Universal Image Segmentation.

Features

  • OneFormer is the first multi-task universal image segmentation framework based on transformers.
  • OneFormer needs to be trained only once with a single universal architecture, a single model, and on a single dataset , to outperform existing frameworks across semantic, instance, and panoptic segmentation tasks.
  • OneFormer uses a task-conditioned joint training strategy, uniformly sampling different ground truth domains (semantic instance, or panoptic) by deriving all labels from panoptic annotations to train its multi-task model.
  • OneFormer uses a task token to condition the model on the task in focus, making our architecture task-guided for training, and task-dynamic for inference, all with a single model.

OneFormer

Contents

  1. News
  2. Installation Instructions
  3. Dataset Preparation
  4. Execution Instructions
  5. Results
  6. Citation

News

November 10, 2022

  • Project Page, ArXiv Preprint and GitHub Repo are public!
  • OneFormer sets new SOTA on Cityscapes val with single-scale inference on Panoptic Segmentation with 68.5 PQ score and Instance Segmentation with 46.7 AP score!
  • OneFormer sets new SOTA on ADE20K val on Panoptic Segmentation with 50.2 PQ score and on Instance Segmentation with 37.6 AP!
  • OneFormer sets new SOTA on COCO val on Panoptic Segmentation with 58.0 PQ score!

Installation Instructions

  • We use Python 3.8, PyTorch 1.10.1 (CUDA 11.3 build).
  • We use Detectron2-v0.6.
  • For complete installation instructions, please see INSTALL.md.

Dataset Preparation

  • We experiment on three major benchmark dataset: ADE20K, Cityscapes and COCO 2017.
  • Please see Preparing Datasets for OneFormer for complete instructions for preparing the datasets.

Execution Instructions

Training

  • We train all our models using 8 A6000 (48 GB each) GPUs.
  • We use 8 A100 (80 GB each) for training Swin-L† OneFormer and DiNAT-L† OneFormer on COCO and all models with ConvNeXt-XL† backbone. We also train the 896x896 models on ADE20K on 8 A100 GPUs.
  • Please see Getting Started with OneFormer for training commands.

Evaluation

Demo

  • We provide a quick to run demo on Colab Open In Colab.
  • Please see OneFormer Demo for command line instructions on running the demo.

Results

Results

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

ADE20K

Method Backbone Crop Size PQ AP mIoU
(s.s)		 mIoU
(ms+flip)		 #params config Checkpoint
OneFormer Swin-L† 640×640 48.6 35.9 57.0 57.7 219M config model
OneFormer Swin-L† 896×896 50.2 37.6 57.4 58.3 219M config model
OneFormer ConvNeXt-L† 640×640 48.7 36.2 56.6 57.4 220M config model
OneFormer DiNAT-L† 640×640 49.1 36.0 57.8 58.4 223M config model
OneFormer DiNAT-L† 896×896 50.0 36.8 58.1 58.6 223M config model
OneFormer ConvNeXt-XL† 640×640 48.9 36.3 57.4 58.8 372M config model

Cityscapes

Method Backbone PQ AP mIoU
(s.s)		 mIoU
(ms+flip)		 #params config Checkpoint
OneFormer Swin-L† 67.2 45.6 83.0 84.4 219M config model
OneFormer ConvNeXt-L† 68.5 46.5 83.0 84.0 220M config model
OneFormer DiNAT-L† 67.6 45.6 83.1 84.0 223M config model
OneFormer ConvNeXt-XL† 68.4 46.7 83.6 84.6 372M config model

COCO

Method Backbone PQ PQTh PQSt AP mIoU #params config Checkpoint
OneFormer Swin-L† 57.9 64.4 48.0 49.0 67.4 219M config model
OneFormer DiNAT-L† 58.0 64.3 48.4 49.2 68.1 223M config model

Citation

If you found OneFormer useful in your research, please consider starring ⭐ us on GitHub and citing 📚 us in your research!

@article{jain2022oneformer,
      title={OneFormer: One Transformer to Rule Universal Image Segmentation},
      author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi},
      journal={arXiv}, 
      year={2022}
    }

Acknowledgement

We thank the authors of Mask2Former, GroupViT, and Neighborhood Attention Transformer for releasing their helpful codebases.