EventBench: Towards Comprehensive Benchmarking of Event-based MLLMs
[Paper]
- Model: Download from EventGPT-Plus-2B
- Benchmark Dataset: Download from EventBench
pip install -r requirements.txtEdit script/predict.sh and run:
bash script/predict.sh| Parameter | Description | Example |
|---|---|---|
--model_path |
Path to the EventGPT-Plus model | /path/to/EventGPT-Plus-2B |
--model_type |
Model backbone type | qwen or llama |
--chat_template |
Chat template to use | eventgpt_qwen |
--event_data |
Path to event data file (.npz) | /path/to/event_data.npz |
--event_data_type |
Type of event data | v2e |
--event_size_cfg |
Path to event size config YAML | /path/to/event_size_type.yaml |
--query |
Question to ask the model | "How does the child move across the tiled floor?" |
| Parameter | Description | Default |
|---|---|---|
--use_npz |
Use npz format for event data | False |
--use_preprocess |
Use preprocessed event data | False |
--compute_ttft |
Compute Time to First Token | False |
--temperature |
Sampling temperature | 0.3 |
--top_p |
Top-p sampling threshold | 1.0 |
--num_beams |
Number of beams for beam search | 1 |
--max_new_tokens |
Maximum tokens to generate | 512 |
--context_max_len |
Maximum context length | 1024 |
--num_bins_list |
List of event bin counts | [4, 8, 16, 32] |
python inference_eventgpt_plus.py \
--model_path /path/to/EventGPT-Plus-2B \
--model_type qwen \
--use_npz \
--use_preprocess \
--event_data_type v2e \
--chat_template eventgpt_qwen \
--event_data /path/to/event_data.npz \
--event_size_cfg /path/to/event_size_type.yaml \
--query "How does the child move across the tiled floor?"If you use EventBench in your research, please cite:
@article{liu2025eventbench,
title={EventBench: Towards Comprehensive Benchmarking of Event-based MLLMs},
author={Liu, Shaoyu and Li, Jianing and Zhao, Guanghui and Zhang, Yunjian and Ji, Xiangyang},
journal={arXiv preprint arXiv:2511.18448},
year={2025}
}