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--- |
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license: mit |
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datasets: |
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- Qingyun/lmmrotate-sft-data |
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language: |
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- en |
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base_model: |
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- microsoft/Florence-2-large |
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pipeline_tag: image-text-to-text |
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tags: |
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- aerial |
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- geoscience |
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- remotesensing |
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--- |
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<p align="center"> |
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<h1 align="center">LMMRotate 🎮: A Simple Aerial Detection Baseline of Multimodal Language Models</h1> |
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<p align="center"> |
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<a href='https://scholar.google.com/citations?hl=en&user=TvsTun4AAAAJ' style='text-decoration: none' >Qingyun Li</a><sup></sup>  |
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<a href='https://scholar.google.com/citations?user=A39S7JgAAAAJ&hl=en' style='text-decoration: none' >Yushi Chen</a><sup></sup>  |
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<a href='https://www.researchgate.net/profile/Shu-Xinya' style='text-decoration: none' >Xinya Shu</a><sup></sup>  |
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<a href='https://scholar.google.com/citations?hl=en&user=UzPtYnQAAAAJ' style='text-decoration: none' >Dong Chen</a><sup></sup>  |
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<a href='https://scholar.google.com/citations?hl=en&user=WQgE8l8AAAAJ' style='text-decoration: none' >Xin He</a><sup></sup>  |
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<a href='https://scholar.google.com/citations?user=OYtSc4AAAAAJ&hl=en' style='text-decoration: none' >Yi Yu</a><sup></sup>  |
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<a href='https://yangxue0827.github.io/' style='text-decoration: none' >Xue Yang</a><sup></sup>  |
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<p align='center'> |
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If you find our work helpful, please consider giving us a ⭐! |
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</p> |
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</p> |
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</p> |
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- ArXiv Paper: https://arxiv.org/abs/2501.09720 |
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- GitHub Repo: https://github.com/Li-Qingyun/mllm-mmrotate |
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- HuggingFace Page: https://huggingface.co/collections/Qingyun/lmmrotate-6780cabaf49c4e705023b8df |
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This repo hosts all the available checkpoints of Florence-2 trained for aerial detection with LMMRotate in [our paper](https://arxiv.org/abs/2501.09720). |
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LMMRotate is a technical practice to fine-tune Large Multimodal language Models for oriented object detection as in MMRotate and hosts the official implementation of the paper: A Simple Aerial Detection Baseline of Multimodal Language Models. |
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<img src="https://github.com/user-attachments/assets/d34e4c0c-9e04-446e-a511-2e7005e32074" alt="framework" width="100%" /> |
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## Detection Performance |
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