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---
library_name: transformers
license: mit
tags:
- vision
- image-segmentation
- pytorch
---
# EoMT
[](https://pytorch.org/)
**EoMT (Encoder-only Mask Transformer)** is a Vision Transformer (ViT) architecture designed for high-quality and efficient image segmentation. It was introduced in the CVPR 2025 highlight paper:
**[Your ViT is Secretly an Image Segmentation Model](https://www.tue-mps.org/eomt)**
by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, and Daan de Geus.
> **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with additional few params can perform segmentation without the need for task-specific decoders or pixel fusion modules. The same model backbone supports semantic, instance, and panoptic segmentation with different post-processing 🤗
The original implementation can be found in this [repository](https://github.com/tue-mps/eomt).
The HuggingFace model page is available at this [link](https://huggingface.co/papers/2503.19108).
---
## Citation
If you find our work useful, please consider citing us as:
```bibtex
@inproceedings{kerssies2025eomt,
author = {Kerssies, Tommie and Cavagnero, Niccolò and Hermans, Alexander and Norouzi, Narges and Averta, Giuseppe and Leibe, Bastian and Dubbelman, Gijs and de Geus, Daan},
title = {Your ViT is Secretly an Image Segmentation Model},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025},
}
```
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