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This repo contains the code for our paper Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

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Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

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This repo contains the code for our paper Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP


FC-CLIP is an universal model for open-vocabulary image segmentation problems, consisting of a class-agnostic segmenter, in-vocabulary classifier, out-of-vocabulary classifier. With everything built upon a shared single frozen convolutional CLIP model, FC-CLIP not only achieves state-of-the-art performance on various open-vocabulary segmentation benchmarks, but also enjoys a much lower training (3.2 days with 8 V100) and testing costs compared to prior arts.

Installation

See installation instructions.

Getting Started

See Preparing Datasets for FC-CLIP.

See Getting Started with FC-CLIP.

We also support FC-CLIP with HuggingFace 🤗 Demo

Model Zoo

ADE20K(A-150) Cityscapes Mapillary Vistas ADE20K-Full
(A-847)
Pascal Context 59
(PC-59)
Pascal Context 459
(PC-459)
Pascal VOC 21
(PAS-21)
Pascal VOC 20
(PAS-20)
COCO
(training dataset)
download
PQ mAP mIoU PQ mAP mIoU PQ mIoU mIoU mIoU mIoU mIoU mIoU PQ mAP mIoU
FC-CLIP (ResNet50) 17.9 9.5 23.3 40.3 21.6 53.2 15.9 24.4 7.1 50.5 12.9 75.9 89.5 50.7 40.7 58.8 checkpoint
FC-CLIP (ResNet101) 19.1 10.2 24.0 40.9 24.1 53.9 16.7 23.2 7.7 48.9 12.3 77.6 91.3 51.4 41.6 58.9 checkpoint
FC-CLIP (ResNet50x4) 21.8 11.7 26.8 42.2 23.8 54.6 17.4 24.6 8.7 54.0 13.1 79.0 92.9 52.1 42.8 60.4 checkpoint
FC-CLIP (ResNet50x16) 22.5 13.6 29.4 42.0 25.6 56.0 17.8 26.1 10.3 56.4 15.7 80.7 94.5 54.4 45.0 63.3 checkpoint
FC-CLIP (ResNet50x64) 22.8 13.6 28.4 42.7 27.4 55.1 18.2 27.3 10.8 55.7 16.2 80.3 95.1 55.6 46.4 65.3 checkpoint
FC-CLIP (ConvNeXt-Large) 26.8 16.8 34.1 44.0 26.8 56.2 18.3 27.8 14.8 58.4 18.2 81.8 95.4 54.4 44.6 63.7 checkpoint

Citing FC-CLIP

If you use FC-CLIP in your research, please use the following BibTeX entry.

@inproceedings{yu2023fcclip,
  title={Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP},
  author={Qihang Yu and Ju He and Xueqing Deng and Xiaohui Shen and Liang-Chieh Chen},
  journal={arXiv: 2308.02487},
  year={2023}
}

Acknowledgement

Mask2Former

ODISE

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This repo contains the code for our paper Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

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  • Python 88.2%
  • Cuda 10.5%
  • C++ 1.2%
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