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videomt-dinov2-base-ytvis2019
tue-mps/videomt-dinov2-base-ytvis2019
videomt-dinov2-base-ytvis2019 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 yt2019vitbase58.2.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
- 914036bf
Model card
From the Hugging Face model README
VidEoMT-B on YouTube-VIS 2019
This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint
yt_2019_vit_base_58.2.pth from tue-mps/VidEoMT.
Model details
- Architecture: VidEoMT with a DINOv2 ViT-B/14 with 4 register tokens backbone
- Task: video instance segmentation
- Dataset: YouTube-VIS 2019
- Input resolution: 640 x 640
- Number of frames: 2
- Paper: Your ViT is Secretly Also a Video Segmentation Model
Reported metrics
| Metric | Value |
|---|---|
| AP | 58.2 |
| AR@10 | 66.5 |
| FPS | 251 |
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-base-ytvis2019"
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