Create README.md

README.md

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+---
+license: bsd-3-clause
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+- facial-forgery-detection
+- multi-label-classification
+- vit
+- deepfake
+- acl-2026
+---
+
+# Face-ViT: Multi-Label Facial Forgery Region Classifier
+
+## 📖 Model Description
+This is the **Face-ViT** auxiliary perception module proposed in the ACL 2026 paper: 
+*"Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline"*.
+
+Face-ViT is a multi-label classifier based on the **ViT-H/14** architecture. It is specifically trained to recognize 21 different types of facial manipulations (e.g., eye modification, skin smoothing, mouth tampering). In the DFF framework, it provides fine-grained visual cues that guide the large language model to generate accurate forensic explanations.
+
+## 🛠️ Model Details
+- **Architecture**: ViT-H/14 with an additional CNN branch and max-pooling for multi-label support.
+- **Input Size**: 224x224 RGB images.
+- **Number of Classes**: 21 (Facial attributes/manipulation types).
+- **Training Objective**: Joint loss including BCE, Focal, Dice, and Jaccard loss.
+
+## 🚀 Links
+- **Official Code**: [Generating-Attribution-Reports](https://github.com/JingchunLian/Generating-Attribution-Reports)
+- **Main Framework (DFF)**: [LianJC/DFF-InstructBLIP-Detection](https://huggingface.co/LianJC/DFF-InstructBLIP-Detection)
+- **Dataset (MMTT)**: [LianJC/MMTT-Dataset](https://huggingface.co/datasets/LianJC/MMTT-Dataset)
+
+## 📜 Citation
+If you find this model useful, please cite:
+```bibtex
+@inproceedings{lian2026generating,
+  title={Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline},
+  author={Lian, Jingchun and others},
+  booktitle={Proceedings of ACL},
+  year={2026},
+  note={To appear}
+}