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videomt-dinov2-large-vipseg
tue-mps/videomt-dinov2-large-vipseg
videomt-dinov2-large-vipseg is a image segmentation model from tue-mps. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint vipsegvitlarge55.2.pth from tue-mps/VidEoMT.
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- Model type
- videomt
- Access
- Public
- Created
- Mar 27, 2026
- Updated
- Mar 27, 2026
- SHA
- c8f89367
Model card
From the Hugging Face model README
VidEoMT-L on VIPSeg
This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint
vipseg_vit_large_55.2.pth from tue-mps/VidEoMT.
Model details
- Architecture: VidEoMT with a DINOv2 ViT-L/14 with 4 register tokens backbone
- Task: video panoptic segmentation
- Dataset: VIPSeg
- Input resolution: 1280 x 1280
- Number of frames: 2
- Paper: Your ViT is Secretly Also a Video Segmentation Model
Reported metrics
| Metric | Value |
|---|---|
| VPQ | 55.2 |
| STQ | 48.9 |
| FPS | 75 |
The metrics above are the numbers reported by the authors in the official model zoo.
Usage
from transformers import AutoModelForUniversalSegmentation, AutoVideoProcessor
model_id = "tue-mps/videomt-dinov2-large-vipseg"
processor = AutoVideoProcessor.from_pretrained(model_id)
model = AutoModelForUniversalSegmentation.from_pretrained(model_id)
Use processor.post_process_instance_segmentation,
processor.post_process_panoptic_segmentation, or
processor.post_process_semantic_segmentation depending on the target task.
- Task
- Image Segmentation
- Library
- transformers
- Type
- videomt
- Created
- Mar 27, 2026
- Updated
- Mar 27, 2026