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facebookresearch / ov-seg

This is the official PyTorch implementation of the paper Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP.
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[OVSeg] Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

This is the official PyTorch implementation of our paper:
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, Diana Marculescu
Computer Vision and Pattern Recognition Conference (CVPR), 2023

[arXiv] [Project] [huggingface demo]

Installation

Please see installation guide.

Data Preparation

Please see datasets preparation.

Getting started

Please see getting started instruction.

Finetuning CLIP

Please see open clip training.

LICENSE

Shield: CC BY-NC 4.0

The majority of OVSeg is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC BY-NC 4.0

However portions of the project are under separate license terms: CLIP and ZSSEG are licensed under the MIT license; MaskFormer is licensed under the CC-BY-NC; openclip is licensed under the license at its repo.

Citing OVSeg :pray:

If you use OVSeg in your research or wish to refer to the baseline results published in the paper, please use the following BibTeX entry.

@inproceedings{liang2023open,
  title={Open-vocabulary semantic segmentation with mask-adapted clip},
  author={Liang, Feng and Wu, Bichen and Dai, Xiaoliang and Li, Kunpeng and Zhao, Yinan and Zhang, Hang and Zhang, Peizhao and Vajda, Peter and Marculescu, Diana},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7061--7070},
  year={2023}
}