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+*This model was published in HF papers on 2025-03-24 and contributed to Hugging Face Transformers on 2026-02-02.*
+
+# EoMT-DINOv3
+
+<div class="flex flex-wrap space-x-1">
+  <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The **EoMT-DINOv3** family extends the [Encoder-only Mask Transformer](eomt) architecture with
+Vision Transformers that are pre-trained using [DINOv3](dinov3). The update delivers stronger segmentation quality across ADE20K and COCO
+benchmarks while preserving the encoder-only design that made EoMT attractive for real-time applications.
+
+Compared to the DINOv2-based models, the DINOv3 variants leverage rotary position embeddings, optional gated MLP blocks
+and the latest pre-training recipes from Meta AI. These changes yield measurable performance gains across semantic,
+instance and panoptic segmentation tasks, as highlighted in the [DINOv3 model zoo](https://github.com/tue-mps/eomt/blob/master/model_zoo/dinov3.md).
+
+The original EoMT architecture was introduced in the CVPR 2025 Highlight paper *[Your ViT is Secretly an Image
+Segmentation Model](https://huggingface.co/papers/2503.19108)* by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans,
+Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman and Daan de Geus. The DINOv3 upgrade keeps the same
+lightweight segmentation head and query-based inference strategy while swapping the encoder for DINOv3 ViT checkpoints.
+
+Tips:
+
+* The configuration exposes DINOv3-specific knobs such as `rope_theta` and `use_gated_mlp`. Large DINOv3 backbones
+  such as `dinov3-vitg14` expect `use_gated_mlp=True`.
+* DINOv3 models can operate on a broader range of resolutions thanks to rotary position embeddings. The image processor
+  still defaults to square crops but custom sizes can be supplied through `AutoImageProcessor`.
+* The pre-trained checkpoints hosted by the TU/e Mobile Perception Systems Lab provide delta weights that should be
+  combined with the upstream DINOv3 backbones. The conversion utilities in the
+  [official repository](https://github.com/tue-mps/eomt) describe this workflow in detail.
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr).
+The original code can be found [here](https://github.com/tue-mps/eomt).
+
+## Usage examples
+
+Below is a minimal example showing how to run panoptic segmentation with a DINOv3-backed EoMT model. The same
+image processor can be reused for semantic or instance segmentation simply by swapping the checkpoint.
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoImageProcessor, AutoModelForUniversalSegmentation
+
+
+model_id = "tue-mps/eomt-dinov3-coco-panoptic-base-640"
+processor = AutoImageProcessor.from_pretrained(model_id)
+model = AutoModelForUniversalSegmentation.from_pretrained(model_id).to("cuda" if torch.cuda.is_available() else "cpu", device_map="auto")
+
+image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    outputs = model(**inputs)
+
+segmentation = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+list(segmentation.keys())
+['segmentation', 'segments_info']
+```
+
+## EomtDinov3Config
+
+[[autodoc]] EomtDinov3Config
+
+## EomtDinov3PreTrainedModel
+
+[[autodoc]] EomtDinov3PreTrainedModel
+    - forward
+
+## EomtDinov3ForUniversalSegmentation
+
+[[autodoc]] EomtDinov3ForUniversalSegmentation