AdaptLLM/food-VQA-benchmark

Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)

This repos contains the food visual instruction tasks for evaluating MLLMs in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.

The main project page is: Adapt-MLLM-to-Domains

1. Download Data

You can load datasets using the datasets library:

from datasets import load_dataset

# Choose the task name from the list of available tasks
task_name = 'FoodSeg103'  # Options: 'Food101', 'FoodSeg103', 'Nutrition5K', 'Recipe1M'

# Load the dataset for the chosen task
data = load_dataset('AdaptLLM/food-VQA-benchmark', task_name, split='test')

print(list(data)[0])

The mapping between category names and indices for Food101, FoodSeg103, and Nutrition5K datasets is provided in the following files:

Click to expand

• Food101: food101_name_to_label_map.json
• FoodSeg103: foodSeg103_id2label.json
• Nutrition5K: nutrition5k_ingredients.py

Example Usages:

Food101

import json

# Load the mapping file
map_path = 'food101_name_to_label_map.json'
name_to_label_map = json.load(open(map_path))
name_to_label_map = {key.replace('_', ' '): value for key, value in name_to_label_map.items()}

# Reverse mapping: label to name
label_to_name_map = {value: key for key, value in name_to_label_map.items()}

FoodSeg103

import json

# Load the mapping file
map_path = 'foodSeg103_id2label.json'
id2name_map = json.load(open(map_path))

# Remove background and irrelevant labels
id2name_map.pop("0")  # Background
id2name_map.pop("103")  # Other ingredients

# Convert keys to integers
id2name_map = {int(key): value for key, value in id2name_map.items()}

# Create reverse mapping: name to ID
name2id_map = {value: key for key, value in id2name_map.items()}

Nutrition5K

from nutrition5k_ingredients import all_ingredients

# Create mappings
id2name_map = dict(zip(range(0, len(all_ingredients)), all_ingredients))
name2id_map = {value: key for key, value in id2name_map.items()}

2. Evaluate Any MLLM Compatible with vLLM on the Food Benchmarks

We provide a guide to directly evaluate MLLMs such as LLaVA-v1.6 (open-source version), Qwen2-VL-Instruct, and Llama-3.2-Vision-Instruct.
To evaluate other MLLMs, refer to this guide for modifying the BaseTask class in the vllm_inference/utils/task.py file.
Feel free reach out to us for assistance!

The dataset loading script is embedded in the inference code, so you can directly run the following commands to evaluate MLLMs.

1) Setup

Install vLLM using pip or from source.

As recommended in the official vLLM documentation, install vLLM in a fresh new conda environment:

conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm  # Ensure vllm>=0.6.2 for compatibility with Llama-3.2. If Llama-3.2 is not used, vllm==0.6.1 is sufficient.

Clone the repository and navigate to the inference directory:

git clone https://github.com/bigai-ai/QA-Synthesizer.git
cd QA-Synthesizer/vllm_inference
RESULTS_DIR=./eval_results  # Directory for saving evaluation scores

2) Evaluate

Run the following commands:

# Specify the domain: choose from ['food', 'Recipe1M', 'Nutrition5K', 'Food101', 'FoodSeg103']
# 'food' runs inference on all food tasks; others run on individual tasks.
DOMAIN='food'

# Specify the model type: choose from ['llava', 'qwen2_vl', 'mllama']
# For LLaVA-v1.6, Qwen2-VL, and Llama-3.2-Vision-Instruct, respectively.
MODEL_TYPE='qwen2_vl'

# Set the model repository ID on Hugging Face. Examples:
# "Qwen/Qwen2-VL-2B-Instruct", "AdaptLLM/food-Qwen2-VL-2B-Instruct" for MLLMs based on Qwen2-VL-Instruct.
# "meta-llama/Llama-3.2-11B-Vision-Instruct", "AdaptLLM/food-Llama-3.2-11B-Vision-Instruct" for MLLMs based on Llama-3.2-Vision-Instruct.
# "AdaptLLM/food-LLaVA-NeXT-Llama3-8B" for MLLMs based on LLaVA-v1.6.
MODEL=AdaptLLM/food-Qwen2-VL-2B-Instruct

# Set the directory for saving model prediction outputs:
OUTPUT_DIR=./output/AdaMLLM-food-Qwen-2B_${DOMAIN}

# Run inference with data parallelism; adjust CUDA devices as needed:
CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' bash run_inference.sh ${MODEL} ${DOMAIN} ${MODEL_TYPE} ${OUTPUT_DIR} ${RESULTS_DIR}

Detailed scripts to reproduce our results are in Evaluation.md

3) Results

The evaluation results are stored in ./eval_results, and the model prediction outputs are in ./output.

Citation

If you find our work helpful, please cite us.

Adapt MLLM to Domains (EMNLP 2025 Findings)

@article{adamllm,
  title={On Domain-Adaptive Post-Training for Multimodal Large Language Models},
  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
  journal={arXiv preprint arXiv:2411.19930},
  year={2024}
}

Adapt LLM to Domains (ICLR 2024)

@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}