The following core steps need to be followed to evaluate the performance of a multimodal large model using PhysUniBenchmark:
- environmental preparation: Clone your GitHub repository (git clone https://github.com/PrismaX-Team/PhysUniBenchmark.git), install Python 3.8+ and configure dependencies (via requirements.txt)
- Data Acquisition: Download the dataset from the project's data folder or follow the documentation for the full dataset
- Model deployment: Ensure that the target model (e.g., GPT-4o, LLaVA) has been deployed, either through an API or a local call to the
- Operational assessment: Use the evaluate.py script (example command: python evaluate.py -model -data_path data/ -output results/)
- Analysis of results: Generate visualization reports via visualize.py to see the model's accuracy and error analysis in different physical domains
Precautions include: it is recommended to use GPU devices to accelerate inference, ensure sufficient storage space (≥10GB), and the cloud API needs to be configured with the correct key. The evaluation report will be output in CSV/JSON format, containing detailed performance statistics and comparison data.
This answer comes from the articlePhysUniBenchmark: benchmarking tool for multimodal physics problemsThe
May not be reproduced without permission:AI productivity tools " How can I use PhysUniBenchmark to evaluate the performance of multimodal large models?
