Add pipeline tag and improve model card

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the model card for FakeReasoning. It adds:
- The `pipeline_tag: image-text-to-text` to the metadata to ensure the model is correctly categorized in the Hugging Face Hub.
- Links to the paper, project page, and the official GitHub repository.
- A sample usage section based on the instructions found in the repository's README.
- The BibTeX citation for researchers to cite the work.

Please review and merge if this looks good!

README.md

@@ -1,17 +1,49 @@
 ---
 license: apache-2.0
+pipeline_tag: image-text-to-text
 ---
-# FakeReasoning Model Card
 
-## Model details
+# FakeReasoning
 
-**Model type:** FakeReasoning is a forgery detection and reasoning framework with three key components: 1) a dual-branch visual encoder that integrates CLIP and DINO to capture both high-level semantics and low-level artifacts; 2) a Forgery-Aware Feature Fusion Module that leverages DINO's attention maps and cross-attention mechanisms to guide MLLMs toward forgery-related clues; 3) a Classification Probability Mapper that couples language modeling and forgery detection, enhancing overall performance. 
+FakeReasoning is a forgery detection and reasoning framework designed to accurately detect AI-generated images and provide reliable reasoning over forgery attributes. It formulates detection and explanation as a unified Forgery Detection and Reasoning task (FDR-Task).
 
-**Project page:** https://pris-cv.github.io/FakeReasoning/
+- **Project Page:** [https://pris-cv.github.io/FakeReasoning/](https://pris-cv.github.io/FakeReasoning/)
+- **Paper:** [Toward Generalizable Forgery Detection and Reasoning](https://huggingface.co/papers/2503.21210)
+- **Code:** [https://github.com/PRIS-CV/FakeReasoning](https://github.com/PRIS-CV/FakeReasoning)
 
-**Paper:** https://arxiv.org/abs/2503.21210
+## Model Details
 
-## License
+FakeReasoning consists of three key components:
+1.  **Dual-branch visual encoder:** Integrates CLIP and DINO to capture both high-level semantics and low-level artifacts.
+2.  **Forgery-Aware Feature Fusion Module:** Leverages DINO's attention maps and cross-attention mechanisms to guide the model toward forgery-related clues.
+3.  **Classification Probability Mapper:** Couples language modeling and forgery detection, enhancing overall performance.
 
-FakeReasoning is licensed under the Apache 2.0 License.
+The model was trained on the **MMFR-Dataset**, a large-scale dataset containing 120K images across 10 generative models with 378K reasoning annotations.
 
+## Sample Usage
+
+To use the model, please follow the installation instructions in the [official repository](https://github.com/PRIS-CV/FakeReasoning). You can then run inference using the following commands:
+
+```bash
+cd LLaVA/forgery_eval
+export DINO_PATH='path_to_dinov2-main'
+export DINO_WEIGHT='path_to_dinov2_vitl14_pretrain.pth'
+
+python inference.py \
+--model-path AnnaGao/FakeReasoning \
+--img_path path_to_your_image.png
+```
+
+Note: Inference and evaluation require at least 30 GB of GPU memory on a single GPU.
+
+## Citation
+
+```bibtex
+@article{gao2025fakereasoning,
+  title={FakeReasoning: Towards Generalizable Forgery Detection and Reasoning},
+  author={Gao, Yueying and Chang, Dongliang and Yu, Bingyao and Qin, Haotian and Chen, Lei and Liang, Kongming and Ma, Zhanyu},
+  journal={arXiv preprint arXiv:2503.21210},
+  year={2025},
+  url={https://arxiv.org/abs/2503.21210}
+}
+```