Update README.md

README.md

@@ -1,6 +1,76 @@
 ---
+library_name: transformers
 license: mit
-pipeline_tag: image-segmentation
+tags:
+- vision
+- image-segmentation
+- pytorch
 ---
+# EoMT
 
-This repository contains the model described in [Your ViT is Secretly an Image Segmentation Model](https://huggingface.co/papers/2503.19108).
+[![PyTorch](https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white)](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)
+
+---
+
+
+### How to use
+
+Here is how to use this model for Panotpic Segmentation:
+
+```python
+import matplotlib.pyplot as plt
+import requests
+import torch
+from PIL import Image
+
+from transformers import EomtForUniversalSegmentation, AutoImageProcessor
+
+
+model_id = "tue-mps/coco_panoptic_eomt_giant_1280"
+processor = AutoImageProcessor.from_pretrained(model_id)
+model = EomtForUniversalSegmentation.from_pretrained(model_id)
+
+image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+inputs = processor(
+    images=image,
+    return_tensors="pt",
+)
+
+with torch.inference_mode():
+    outputs = model(**inputs)
+
+# Prepare the original image size in the format (height, width)
+target_sizes = [(image.height, image.width)]
+
+# Post-process the model outputs to get final segmentation prediction
+preds = processor.post_process_panoptic_segmentation(
+    outputs,
+    target_sizes=target_sizes,
+)
+
+# Visualize the panoptic segmentation mask
+plt.imshow(preds[0]["segmentation"])
+plt.axis("off")
+plt.title("Panoptic Segmentation")
+plt.show()
+```
+
+## 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},
+}
+```