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--- |
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inference: false |
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pipeline_tag: image-text-to-text |
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--- |
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<br> |
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<br> |
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# Finedefics Model Card |
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## Model details |
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**Model type:** |
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Finedefics is an open-source MLLM that enhances the model's FGVR capability by incorporating informative attribute descriptions of objects into the training phase. |
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It is an auto-regressive language model, based on the transformer architecture. |
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Base MLLM: [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) |
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**Paper or resources for more information:** |
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OpenReview: https://openreview.net/forum?id=p3NKpom1VL |
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Arxiv: https://arxiv.org/abs/2501.15140 |
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## License |
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Idefics2 is licensed under the Apache 2.0 license, and we release the Finedefics checkpoints under the same license. |
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**Where to send questions or comments about the model:** |
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https://github.com/PKU-ICST-MIPL/Finedefics_ICLR2025/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of Finedefics is research on Fine-grained MLLM. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training and evaluation datasets |
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A collection of 6 fine-grained visual recognition datasets, including Stanford Dog-120, Bird-200, FGVC-Aircraft, Flower-102, Oxford-IIIT Pet-37, and Stanford Car-196. |
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