DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models

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🔥 Release

• [2025/11/26] RPM-10K dataset & DialBench benchmark coming soon
• [2025/11/26] Model weights coming soon

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✨ Highlights

• ✅ RPM-10K: large-scale pointer meter reading dataset
• ✅ DialBench: evaluation benchmark for large foundation models
• ✅ Simple and strong multimodal baseline for pointer meter reading

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📦 Dataset: RPM-10K

RPM-10K is designed for accurate and robust pointer meter reading.

• Scale: 10,730 images
• Focus: diverse real-world pointer meters

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🧪 Benchmark: DialBench

DialBench provides a comprehensive benchmark for evaluating pointer meter reading in multimodal LLMs / VLMs.

Features:

• Multiple metrics: Acc_ε, Acc_θ, Ref↓, Rel↓\
• Comparison to both open-source and closed-source VLMs

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🧩 Model Zoo / Weights

• Our Model Weights (TBD): ()\

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🛠 Installation

1. Create conda environment

conda create -n dialbench python=3.9
conda activate dialbench

2. Install from source

git clone https://github.com/Event-AHU/DialBench.git
cd DialBench
pip install -e .

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🚀 Training

Run:

bash train.sh

• Modify dataset paths in 'caption_builder.py'

  datasets['train'] = dataset_cls(
              vis_processor=self.vis_processors["train"],
              text_processor=self.text_processors["train"],
              ann_paths=[os.path.join(storage_path, '')], 
              vis_root=vis_root,
          )

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🧪 Testing / Evaluation

bash test.sh

• Evaluation settings are also directly controlled via test.sh

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📌 Citation

If you find DialBench useful:

@misc{your2025dialbench,
  title={DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models},
  author={Your Name and ...},
  year={2025},
  eprint={xxxx.xxxxx},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

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🙏 Acknowledgements

• BLIVA
• BLIP-2
• LAVIS
• All open-source contributors

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📄 License

• Code: BSD 3-Clause License
• Dataset: TBD
• Model weights: TBD