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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- liuhaotian/llava-v1.6-mistral-7b |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- llm |
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- mllm |
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- deepfake |
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--- |
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# FFAA Model Card |
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## Model details |
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**Model type**: Face Forgery Analysis Assistant (FFAA) consists of a fine-tuned MLLM and Multi-answer Intelligent Decision System (MIDS). |
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It is a Multi-modal Large Language Model dedicated to the face forgery analysis. |
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Base MLLM: [liuhaotian/llava-v1.6-mistral-7b](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b) |
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**Paper or resources for more information**: [https://ffaa-vl.github.io/](https://ffaa-vl.github.io/) |
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**Where to send questions or comments about the model**: [https://github.com/thu-huangzc/FFAA/issues](https://github.com/thu-huangzc/FFAA/issues) |
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## Intended use |
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**Primary intended uses**: The primary use of FFAA is research on the applications of MLLMs in face forgery analysis, which is essential |
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for understanding the model’s decision-making process and advancing real-world face forgery analysis. |
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**Primary intended users**: The primary intended users of the model are researchers and hobbyists in computer vision, |
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natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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* 20K face forgery analysis VQA (FFA-VQA) dataset, captioned by GPT-4o. |
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* 90K historical answer data generated by the MLLM fine-tuned on FFA-VQA. |
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## Evaluation dataset |
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Open-World Face Forgery Analysis Benchmark (OW-FFA-Bench), including 6 face forgery generalization test sets. |
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The download link is [Google driver](https://drive.google.com/file/d/1867ZKwFCh_OLm-uUsiIiI9RjrUv0JMZX/view?usp=drive_link) |
