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README.md
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**If you use this model, please pay attention to our work: LandGPT: A Multimodal Large Language Model for Parcel-Level Land Use Classification with Multi-Source Data (under submission).**
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To reproduce the main contributions of the paper, please download this model and configure the model runtime library according to `
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In addition, a proper JSONL file is required as an image index to feed into the model. The code for creating this JSONL file has been open-sourced at `https://figshare.com/s/1eb38bac0cc970698379`
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This will launch the model inference.
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# LandGPT
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**此仓库为LandGPT-8B和相应的模型推理代码**
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**如使用此模型,请关注我们的工作:LandGPT: A Multimodal Large Language Model for Parcel-Level Land Use Classification with Multi-Source Data(投稿中)**
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如复现文章主要工作,请下载此模型,并根据`
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此外,还需要合适的jsonl文件作为图像索引送入模型,制作此jsonl文件的代码已经开源在`https://figshare.com/s/1eb38bac0cc970698379`。
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python true_pre_2level.py
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```
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即可启动模型推理。
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**If you use this model, please pay attention to our work: LandGPT: A Multimodal Large Language Model for Parcel-Level Land Use Classification with Multi-Source Data (under submission).**
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To reproduce the main contributions of the paper, please download this model and configure the model runtime library according to `pip install lmdeploy>=0.5.3`.
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In addition, a proper JSONL file is required as an image index to feed into the model. The code for creating this JSONL file has been open-sourced at `https://figshare.com/s/1eb38bac0cc970698379`
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This will launch the model inference.
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We recommend using a computing card with at least 24GB of VRAM for model inference, as insufficient VRAM may affect the inference quality.
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# LandGPT
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**此仓库为LandGPT-8B和相应的模型推理代码**
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**如使用此模型,请关注我们的工作:LandGPT: A Multimodal Large Language Model for Parcel-Level Land Use Classification with Multi-Source Data(投稿中)**
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如复现文章主要工作,请下载此模型,并根据` pip install lmdeploy>=0.5.3`配置模型运行库。
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此外,还需要合适的jsonl文件作为图像索引送入模型,制作此jsonl文件的代码已经开源在`https://figshare.com/s/1eb38bac0cc970698379`。
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python true_pre_2level.py
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```
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即可启动模型推理。
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我们建议使用至少24GB显存的计算卡来进行模型推理,过低的显存可能会影响推理质量。
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