tue-mps
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eomt-dinov3-coco-instance-large-1280

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: mit
+tags:
+- vision
+- image-segmentation
+- instance-segmentation
+- pytorch
+pipeline_tag: image-segmentation
+datasets:
+- coco
 ---
+# EoMT-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 EoMT-DINOv3 model trained for **instance segmentation** on COCO at **1280×1280** resolution.
+**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)**
+> **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with a few additional parameters 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 **DINOv3** variants leverage rotary position embeddings and the latest pre-training recipes from Meta AI, yielding measurable performance gains across segmentation tasks.
+## Usage
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, EomtDinov3ForUniversalSegmentation
+model_id = "nielsr/eomt-dinov3-coco-instance-large-1280"
+processor = AutoImageProcessor.from_pretrained(model_id)
+model = EomtDinov3ForUniversalSegmentation.from_pretrained(model_id)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt").to(device)
+with torch.inference_mode():
+    outputs = model(**inputs)
+# Instance Segmentation
+result = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+print(result["segmentation"].shape)  # Segmentation map
+print(result["segments_info"])  # List of detected segments with labels
+```
+## Model Details
+| Property | Value |
+|----------|-------|
+| Backbone | DINOv3 ViT-L/16 |
+| Input Resolution | 1280×1280 |
+| Task | Instance Segmentation |
+| Dataset | COCO |
+## Citation
+```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},
+}
+```
+## Acknowledgements
+- Original implementation: [tue-mps/eomt](https://github.com/tue-mps/eomt)
+- Paper: [arXiv:2503.19108](https://arxiv.org/abs/2503.19108)