BruceFeng98/FUNPosterEval

🧪 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.

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