UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity
Paper • 2511.13714 • Published • 12
How to use yujunwei04/unsam-whole-image-segmentation with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-segmentation", model="yujunwei04/unsam-whole-image-segmentation") # Load model directly
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
processor = AutoImageProcessor.from_pretrained("yujunwei04/unsam-whole-image-segmentation")
model = Mask2FormerForUniversalSegmentation.from_pretrained("yujunwei04/unsam-whole-image-segmentation")Whole image segmentation model for UnSAM (NeurIPS 2024: https://arxiv.org/pdf/2406.20081). The base model is Mask2Former.
Code: https://github.com/frank-xwang/UnSAM
BibTeX:
@article{wang2024segment, title={Segment anything without supervision}, author={Wang, XuDong and Yang, Jingfeng and Darrell, Trevor}, journal={Advances in Neural Information Processing Systems}, volume={37}, pages={138731--138755}, year={2024} }
@article{yu2025unsamv2, title={UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity}, author={Yu, Junwei and Darrell, Trevor and Wang, XuDong}, journal={arXiv preprint arXiv:2511.13714}, year={2025} }