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README.md

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----
-license: cc-by-4.0
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+---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+language:
+- en
+license: cc-by-nc-4.0
+pipeline_tag: image-text-to-text
+tags:
+- image
+datasets:
+- ghost233lism/GeoSeek
+---
+
+<div align="center">
+
+<h1>GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristic</h1>
+
+
+
+[**Modi Jin**](https://ghost233lism.github.io/)<sup>1</sup> · [**Yiming Zhang**](https://zhang-yi-ming.github.io/)<sup>1</sup> · [**Boyuan Sun**](https://bbbbchan.github.io/)<sup>1</sup> · [**Dingwen Zhang**](https://zdw-nwpu.github.io/dingwenz.github.com/)<sup>2</sup> · [**Mingming Cheng**](https://mmcheng.net/)<sup>1</sup> · [**Qibin Hou**](https://houqb.github.io/)<sup>1&dagger;</sup>
+
+<sup>1</sup>VCIP, Nankai University  <sup>2</sup> School of Automation, Northwestern Polytechnical University
+
+&dagger;Corresponding author
+
+**English | [简体中文](README_zh.md)**
+
+  <a href="https://github.com/HVision-NKU/GeoAgent"><img alt="github" src="https://img.shields.io/badge/Github-GeoAgent-181717?logo=github&color=1783ff&logoColor=white"/></a>
+<a href="https://ghost233lism.github.io/GeoAgent-page/"><img src='https://img.shields.io/badge/Project-Page-green' alt='Project Page'></a>
+<a href='https://huggingface.co/datasets/ghost233lism/GeoSeek'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-GeoSeek_Dataset-purple'></a>
+<a href='https://huggingface.co/ghost233lism/GeoAgent'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
+<a href='https://huggingface.co/spaces/ghost233lism/GeoAgent'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-orange' alt='Demo'></a>
+
+</div>
+
+<!-- ![teaser](assets/teaser.png) -->
+
+
+
+**GeoAgent** is a vision-language model for **image geolocation** that reasons closely with humans and derives fine-grained address conclusions. Built upon [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), it achieves strong performance across multiple geographic grains (city, region, country, continent) while generating interpretable chain-of-thought reasoning.
+
+GeoAgent introduces: 
+
+1. **Geo-similarity reward** combining spatial and semantic similarity to handle the many-to-one mapping between natural language and geographic locations; 
+2. **Consistency reward** assessed by a consistency agent to ensure the integrity and consistency of reasoning chains. The model is trained on **GeoSeek**, a novel geolocation dataset with human-annotated CoT and bias-reducing sampling.
+
+We also introduce [**GeoSeek**](https://huggingface.co/datasets/ghost233lism/GeoSeek), which is a new geolocation dataset comprising:
+
+- **GeoSeek-CoT** (10k): High-quality chain-of-thought data labeled by geography experts and professional geolocation game players. Each entry includes street-view images, GPS coordinates, three-level location labels (country, city, precise location), and human reasoning processes—standardized into a unified CoT format.
+- **GeoSeek-Loc** (20k): Images for RL-based finetuning, sampled via a stratified strategy considering population, land area, and highway mileage to reduce geographic bias.
+- **GeoSeek-Val** (3k): Validation benchmark with locatability scores and scene categories (manmade structures, natural landscapes, etc.) for evaluation.
+
+
+
+<!-- <div align="center">
+<img src="assets/depthanything-AC-video.gif" alt="video" width="100%">
+</div> -->
+
+
+<!-- ## Model Architecture -->
+
+<!-- ![architecture](assets/pipeline.png) -->
+
+## Installation
+
+### Requirements
+
+- Python>=3.9
+- torch==2.6.0
+- torchvision==0.21.0
+- torchaudio==2.6.0
+- ms-swift>=3.8.0
+- xformers==0.0.27.post2 
+- deepspeed==0.15.0
+- cuda==12.4
+
+### Setup
+```bash
+git clone https://github.com/HVision-NKU/GeoAgent.git
+cd GeoAgent
+
+conda create -n GeoAgent python=3.9
+conda activate GeoAgent
+pip install -r requirements.txt
+```
+
+## Usage
+### Get GeoAgent Model
+Download the pre-trained checkpoints from [Hugging Face](https://huggingface.co/ghost233lism/GeoAgent):
+```bash
+mkdir checkpoints
+cd checkpoints
+
+# (Optional) Using huggingface mirrors
+export HF_ENDPOINT=https://hf-mirror.com
+
+# download GeoAgent model from huggingface
+huggingface-cli download --resume-download ghost233lism/GeoAgent --local-dir ghost233lism/GeoAgent
+```
+
+### Quick Inference
+
+We provide the quick inference scripts for single/batch image input in `infer/`.  Please refer to [infer/README](https://github.com/HVision-NKU/GeoAgent/infer/README.md) for detailed information.
+
+### Training
+
+
+```bash
+bash tools/train_sft.sh 
+bash tools/train_grpo.sh
+```
+
+
+## Citation
+
+Coming soon...
+
+
+## License
+
+This code is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) for non-commercial use only.
+
+Please note that any commercial use of this code requires formal permission prior to use.
+
+## Contact
+
+For technical questions, please contact jin_modi[AT]mail.nankai.edu.cn
+
+For commercial licensing, please contact andrewhoux[AT]gmail.com.
+
+## Acknowledgments
+
+We sincerely thank [Yue Zhang](https://tuxun.fun/), [H.M.](https://space.bilibili.com/1655209518?spm_id_from=333.337.0.0), [Haowen He](https://space.bilibili.com/111714204?spm_id_from=333.337.0.0), [Yuke Jun](https://space.bilibili.com/93569847?spm_id_from=333.337.0.0), and other experts in geography, as well as outstanding geolocation game players, for their valuable guidance, prompt design suggestions, and data support throughout the construction of the GeoSeek dataset.
+
+We also thank [Zhixiang Wang](https://tuxun.fun/), [Chilin Chen](https://tuxun.fun/), [Jincheng Shi](https://tuxun.fun/), [Liupeng Zhang](https://tuxun.fun/), [Yuan Gu](https://tuxun.fun/), [Yanghang Shao](https://tuxun.fun/), [Jinhua Zhang](https://tuxun.fun/), [Jiachen Zhu](https://tuxun.fun/), [Gucheng Qiuyue](https://tuxun.fun/), [Qingyang Guo](https://tuxun.fun/), [Jingchen Yang](https://tuxun.fun/), [Weilong Kong](https://tuxun.fun/), [Xinyuan Li](https://tuxun.fun/), and [Mr. Xu](https://tuxun.fun/) (an anonymous volunteer) 
+for their outstanding contributions in providing high-quality reasoning process data.