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license: cc-by-nc-4.0
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# 🧪 Multimodal Benchmark
This repository provides a benchmark suite for evaluating Multimodal Large Language Models (MLLMs) across a variety of visual-language tasks.
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## 📁 Directory Structure
### `/data`
This folder contains all benchmark images and task-specific JSON files. Each JSON file defines the input and expected output format for a given task.
### `/run`
This folder includes example scripts that demonstrate how to run different MLLMs on the benchmark tasks.
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## 📄 Result Collection
After inference, all task JSON outputs should be merged into a single file named `result.json`.
Each entry in `result.json` includes a `response` field that stores the model's prediction.
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## 📊 Evaluation
The predictions stored in `result.json` can be evaluated using `metric.py`.
This script computes performance metrics by comparing the predicted responses with the reference answers.
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## 💡 Ad Understanding Task
The **Ad Understanding** task requires an additional LLM-based preprocessing step before evaluation.
An example of deploying a language model for this purpose is provided in [`gpt_judge.py`](./gpt_judge.py).
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