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U2Seg: Unsupervised Universal Image Segmentation

We present U2Seg, a unified framework for Unsupervised Universal image Segmentation that consistently outperforms previous state-of-the-art methods designed for individual tasks: CutLER for unsupervised instance segmentation, STEGO for unsupervised semantic segmentation, and the naive combination of CutLER and STEGO for unsupervised panoptic segmentation.

Unsupervised Universal Image Segmentation
Dantong Niu*, Xudong Wang*, Xinyang Han*, Long Lian, Roei Herzig, Trevor Darrell
Berkeley AI Research, UC Berkeley
CVPR 2024

project page | arxiv | bibtex

Installation

See installation instructions.

Dataset Preparation

See Preparing Datasets for U2Seg.

Method Overview

U2Seg has 4 stages: 1) generating pseudo instance masks with MaskCut and clustering, 2) generating pseudo semantic masks with STEGO and 3) merging instance masks, semantic masks to get pseudo labels for panoptic segmentation and 4) learning unsupervised universal segmentor from pseudo-masks of unlabeled data.

Pseudo Mask Gneration

This part includes MaskCut+Clustering, which we use to generate the pseudo for training of U2Seg, additional information will come later. For implementers who wants to play with out models, we provide well-processed annotations in Data Preparation.

Universal Image Segmentation

Training

After you prepare the dataset following the above instructions, you should be able to train the U2Seg universal segmentation model by:

python ./tools/train_net.py  --config-file ./configs/COCO-PanopticSegmentation/u2seg_R50_800.yaml

Note: you need to download the pre-trained dino backbone and change the path of the corresponding yaml file.

To train U2Seg model with different clustering number (e.g. 300), you can use configs/COCO-PanopticSegmentation/u2seg_R50_300.yaml config file and set the environment variable by export CLUSTER_NUM=300. (This variable would be used in detectron2/data/datasets/builtin.py and detectron2/data/datasets/builtin_meta.py)

Inference

We provide models trained with different cluster numbers and training sets. Each cell in the table below contains a link to the corresponding model checkpoint. Place the downloaded ckpts under ckpts folder.

To run inference on images, you should first assign a checkpoint in the u2seg_eval.yaml, then:

python ./demo/u2seg_demo.py --config-file configs/COCO-PanopticSegmentation/u2seg_eval_800.yaml --input demo/images/*jpg --output results/demo_800 

To test model trained with different clustering number (e.g. 300), you can use config file like this configs/COCO-PanopticSegmentation/u2seg_R50_300.yaml.

We give a few demo images in demo/images and the corresponding visualizations of the panoptic segmentation with U2Seg:

Evaluation

To reproduce the quantitative evaluation results in the main paper, one can following the steps below.

1. Prepare the ground truth annotations

Since the original official panoptic annotations have 53 semantic categories, while u2seg has 15 supercategories, first run get_panoptic_anns_supercategory.py to generate the supercategory version of groudtruth, then you will get panoptic_val2017_300super.json under the detectron2/datasets/datasets/panoptic_anns path.

2. Set the CATEGORIES

User should manually comment out line 42-176 in detectron2/data/datasets/builtin_meta.py when switch from training to evaluation. Also user needs to maunally change the cluster_num in line 39 in detectron2/evaluation/panoptic_evaluation.py based on your own seeting.

3. Get hungarain mapping dict

We do the hungarian matching to map our cluter id to real cagetory id. The user needs to run:

export CLUSTER_NUM=300
python tools/train_net.py --config-file configs/COCO-PanopticSegmentation/u2seg_eval_300.yaml --eval-only --eval-mode hungarian_matching --num-gpus 1

• note that this should be run only by using one single gpu. After that, a folder named hungarian_matching should be gnerated under detectron2/, we provide an hungarian mapping dict example with cluter number is 300 the user can have a quick check of the intermediate results.

4. Get evaluation results for instance segmentation, semantic segmentation and panoptic segmentation

export CLUSTER_NUM=300
python tools/train_net.py --config-file configs/COCO-PanopticSegmentation/u2seg_eval_300.yaml --eval-only --eval-mode eval --num-gpus 8

• this can be executed by multiple gpus.

Efficient Learning

We give the reproduction of our efficient learning part in U2Seg_eff. See that repo if you want to play with this part.

License

U2Seg is licensed under the Apache, however portions of the project are available under separate license terms: CutLER, Detectron2 and DINO are licensed under the CC-BY-NC license; TokenCut, Bilateral Solver and CRF are licensed under the MIT license; If you later add other third party code, please keep this license info updated, and please let us know if that component is licensed under something other than CC-BY-NC, MIT, or CC0.

Ethical Considerations

U2Seg's wide range of detection capabilities may introduce similar challenges to many other visual recognition methods. As the image can contain arbitrary instances, it may impact the model output.

How to get support from us?

If you have any general questions, feel free to email us at Dantong Niu, Xudong Wang, Xinyang Han. If you have code or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others).

Citation

If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation.

@misc{niu2023unsupervised,
      title={Unsupervised Universal Image Segmentation}, 
      author={Dantong Niu and Xudong Wang and Xinyang Han and Long Lian and Roei Herzig and Trevor Darrell},
      year={2023},
      eprint={2312.17243},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}