Datasets:

nvidia
/

VESPER

Minimal code to evaluate a vision-language model on VESPER. It queries the model through an OpenAI-compatible chat-completions API, labels each answer as stop or continue with an LLM judge, and reports the paper's metrics.

Setup

cd code
pip install -r requirements.txt
export VLM_API_BASE="https://your-provider.example"   # /v1/chat/completions is appended
export VLM_API_KEY="<your-api-key>"

If your endpoint's path does not end in /v1/chat/completions, pass the full URL with --api-url. Model names are passed through unchanged, so use the names your provider expects.

Usage

Run the commands from this code/ directory. The annotations and clips are in the parent directory.

# 1. Query the model under the four prompt conditions.
python -m evaluation.run_inference \
    --scenarios ../benchmark.json \
    --model gemini-2.5-pro \
    --output-dir ./eval_results

# 2. Label each answer with the LLM judge. The paper used GPT-5.2.
python -m evaluation.run_judge \
    --output-dir ./eval_results \
    --judge-model gpt-5.2

# 3. Report dangerous (D) and safe (S) accuracy and Geo = sqrt(D * S).
python -m evaluation.summarize_results \
    --output-dir ./eval_results

Settings used in the paper

Models	Visual input
Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 3 Flash	the whole clip as one video (`--input-mode gemini_video`, chosen automatically)
GPT-5.1	32 frames (`--input-mode openai_frames --max-frames 32`)
Claude Sonnet 4.6	16 frames (`--input-mode openai_frames --max-frames 16`)
Kimi K2.5	10 frames (`--input-mode openai_frames --max-frames 10`)

--input-mode auto, the default, sends frames to models whose name contains gpt- or /gpt, and the whole clip to every other model. The whole clip goes as a base64 data:video/mp4 URL in an image_url part; if your endpoint does not accept video that way, use --input-mode openai_frames.
Frames are spread evenly from the first to the last frame of the clip.
Model requests carry only the model name and the messages. Each judge request sends temperature: 0 and max_tokens: 64.

License

The code is released under the NVIDIA OneWay Noncommercial License; see LICENSE. It may be used non-commercially, meaning for research or evaluation purposes only. The dataset in the parent directory is covered by the NVIDIA Evaluation Dataset License Agreement.