Update README.md

README.md

@@ -1,5 +1,46 @@
 ---
 license: mit
-pipeline_tag: image-classification
 library_name: transformers
----
+tags:
+- pytorch
+- image-classification
+---
+
+# Deepfake Detection that Generalizes Across Benchmarks (WACV 2026)
+
+
+[![arXiv Badge](https://img.shields.io/badge/arXiv-B31B1B?logo=arxiv&logoColor=FFF)](https://arxiv.org/abs/2508.06248)
+[![GitHub Badge](https://img.shields.io/badge/GitHub-181717?logo=github&logoColor=fff)](https://github.com/yermandy/GenD)
+
+This is the GenD (PE) model from Tab. 2 in the paper.
+
+## Set up environment
+
+``` bash
+conda create --name GenD python=3.12 uv
+conda activate GenD
+uv pip install -r requirements.txt
+```
+
+## Usage
+
+``` python
+import requests
+import torch
+from PIL import Image
+
+from src.hf.modeling_gend import GenD
+
+model = GenD.from_pretrained("yermandy/GenD_PE_L")
+
+urls = [
+    "https://github.com/yermandy/deepfake-detection/blob/main/datasets/FF/DF/000_003/000.png?raw=true",
+    "https://github.com/yermandy/deepfake-detection/blob/main/datasets/FF/real/000/000.png?raw=true",
+]
+images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
+tensors = torch.stack([model.feature_extractor.preprocess(img) for img in images])
+logits = model(tensors)
+probs = logits.softmax(dim=-1)
+
+print(probs)
+```