OneFormer: One Transformer to Rule Universal Image Segmentation

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.

Contents

1. News
2. Installation Instructions
3. Dataset Preparation
4. Execution Instructions
   • Training
   • Evaluation
   • Demo
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

• Please see Getting Started with OneFormer for evaluation commands.

Demo

• We provide a quick to run demo on Colab .

• Please see OneFormer Demo for command line instructions on running the demo.

Results

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

ADE20K

Cityscapes

COCO

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.