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ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation exploits pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. It leverages the frozen representation of both these models to perform panoptic segmentation of any category in the wild.

This repository is the official implementation of ODISE introduced in the paper:

Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models Jiarui Xu, Sifei Liu*, Arash Vahdat*, Wonmin Byeon, Xiaolong Wang, Shalini De Mello CVPR 2023 Highlight. (*equal contribution)

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing.

teaser

Visual Results

Links

Citation

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

@article{xu2023odise,
  title={{Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models}},
  author={Xu, Jiarui and Liu, Sifei and Vahdat, Arash and Byeon, Wonmin and Wang, Xiaolong and De Mello, Shalini},
  journal={arXiv preprint arXiv:2303.04803},
  year={2023}
}

Environment Setup

Install dependencies by running:

conda create -n odise python=3.9
conda activate odise
conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.6 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-11.6.1" libcusolver-dev
git clone git@github.com:NVlabs/ODISE.git 
cd ODISE
pip install -e .

(Optional) install xformers for efficient transformer implementation: One could either install the pre-built version

pip install xformers==0.0.16

or build from latest source

# (Optional) Makes the build much faster
pip install ninja
# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
# (this can take dozens of minutes)

Model Zoo

We provide two pre-trained models for ODISE trained with label or caption supervision on COCO's entire training set. ODISE's pre-trained models are subject to the Creative Commons — Attribution-NonCommercial-ShareAlike 4.0 International — CC BY-NC-SA 4.0 License terms. Each model contains 28.1M trainable parameters. The download links for these models are provided in the table below. When you run the demo/demo.py or inference script for the very first time, it will also automatically download ODISE's pre-trained model to your local folder $HOME/.torch/iopath_cache/NVlabs/ODISE/releases/download/v1.0.0/.

ADE20K(A-150) COCO ADE20K-Full
(A-847)		 Pascal Context 59
(PC-59)		 Pascal Context 459
(PC-459)		 Pascal VOC 21
(PAS-21) 		download
PQ mAP mIoU PQ mAP mIoU mIoU mIoU mIoU mIoU
ODISE (label) 22.6 14.4 29.9 55.4 46.0 65.2 11.1 57.3 14.5 84.6 checkpoint
ODISE (caption) 23.4 13.9 28.7 45.6 38.4 52.4 11.0 55.3 13.8 82.7 checkpoint

Get Started

See Preparing Datasets for ODISE.

See Getting Started with ODISE for detailed instructions on training and inference with ODISE.

Demo

Important Note: When you run the demo/demo.py script for the very first time, besides ODISE's pre-trained models, it will also automaticlaly download the pre-trained models for Stable Diffusion v1.3 and CLIP, from their original sources, to your local directories $HOME/.torch/ and $HOME/.cache/clip, respectively. The pre-trained models for Stable Diffusion and CLIP are subject to their original license terms from Stable Diffusion and CLIP, respectively.

  • To run ODISE's demo from the command line:

    python demo/demo.py --input demo/examples/coco.jpg --output demo/coco_pred.jpg --vocab "black pickup truck, pickup truck; blue sky, sky"

    The output is saved in demo/coco_pred.jpg. For more detailed options for demo/demo.py see Getting Started with ODISE.

  • To run the Gradio demo locally:

    python demo/app.py

Acknowledgement

Code is largely based on Detectron2, Stable Diffusion, Mask2Former, OpenCLIP and GLIDE.

Thank you, all, for the great open-source projects!