Create README.md

README.md

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+---
+library_name: transformers
+license: mit
+tags:
+- vision
+- image-segmentation
+- instance-segmentation
+- pytorch
+pipeline_tag: image-segmentation
+datasets:
+- coco
+base_model:
+- tue-mps/coco_instance_pmt_large_1280_dinov3
+---
+
+# PMT-DINOv3 (Large, 1280px) for COCO Instance Segmentation
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="Transformers" src="https://img.shields.io/badge/Transformers-yellow?style=flat&logo=huggingface&logoColor=white">
+</div>
+
+## Overview
+
+This is the **large** variant of the PMT-DINOv3 model trained for **instance segmentation** on COCO at **1280x1280** resolution.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Backbone | DINOv3 ViT-L/16 |
+| Input Resolution | 1280x1280 |
+| Task | Instance Segmentation |
+| Dataset | COCO |
+
+## Citation
+
+```bibtex
+@inproceedings{cavagnero2026pmt,
+    author    = {Cavagnero, Niccolò and Norouzi, Narges and Dubbelman, Gijs and de Geus, Daan},
+    title     = {PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
+    year      = {2026},
+}
+```
+
+## Acknowledgements
+
+- Original implementation: [tue-mps/pmt](https://github.com/tue-mps/pmt)
+- Paper: [arXiv:2503.19108](https://arxiv.org/abs/2603.25398)