Improve model card: add pipeline tag, paper link, and usage instructions

README.md

@@ -2,9 +2,47 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+pipeline_tag: image-segmentation
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: https://github.com/MaticFuc/AnomalyVFM
-- Paper: https://arxiv.org/abs/2601.20524
-- Docs: [More Information Needed]
+# AnomalyVFM: Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
+
+AnomalyVFM is a general and effective framework that transforms pretrained Vision Foundation Models (VFMs), such as RADIO, DINOv2, or SigLIP2, into strong zero-shot anomaly detectors. This model was presented at CVPR 2026.
+
+- **Paper:** [AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors](https://huggingface.co/papers/2601.20524)
+- **Repository:** [GitHub](https://github.com/MaticFuc/AnomalyVFM)
+- **Project Page:** [https://maticfuc.github.io/anomaly_vfm/](https://maticfuc.github.io/anomaly_vfm/)
+
+## Overview
+
+Zero-shot anomaly detection aims to detect and localise abnormal regions in images without access to any in-domain training images. AnomalyVFM addresses this by combining a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism using low-rank feature adapters (LoRA/DoRA) and a confidence-weighted pixel loss.
+
+With RADIO as a backbone, AnomalyVFM achieves an average image-level AUROC of 94.1% across 9 diverse datasets, substantially outperforming previous methods.
+
+## Usage
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
+
+### Single Image Prediction
+
+To test the model on a single image, you can use the prediction script provided in the [official repository](https://github.com/MaticFuc/AnomalyVFM):
+
+```bash
+python predict_single_image.py --image-path /path/to/image.png --model-path /path/to/folder/with/model.pkl
+```
+
+By default, the output will be saved as `pred.png`.
+
+## Citation
+
+If this work contributes to your research, please consider citing:
+
+```bibtex
+@InProceedings{fucka2026anomaly_vfm,
+    title={AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors},
+    author={Fučka, Matic and Zavrtanik, Vitjan and Skočaj, Danijel},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2026}
+}
+```