VIEScore
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Visual Instruction-guided Explainable Metric. Code for "Towards Explainable Metrics for Conditional Image Synthesis Evaluation" (ACL 2024 main)
VIEScore
This repository hosts our work's code VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation.
VIEScore is a Visual Instruction-guided Explainable metric for evaluating any conditional image generation tasks.
π₯ π₯ π₯ Check out our [Project Page] for more results and analysis!
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π° News
- 2024 Jan 11: Code is released!
- 2023 Dec 24: Paper available on Arxiv. Code coming Soon!
Project Structure
-
imagen_museum
: helpers to fetch image data from ImagenMuseum -
mllm_tools
: Plug-and-Play MLLMs. -
_questions
: prompt folder -
_answers
: results folder -
run.py
: script to run VIEScore. -
clean_result.py
: script to clear nonsense results according tobanned_reasonings.txt
. -
count_entries.py
: script to count the number of entries.
Running VIEScore
$ python3 run.py --help
usage: run.py [-h] [--task {tie,mie,t2i,cig,sdig,msdig,sdie}] [--mllm {gpt4v,llava,blip2,fuyu,qwenvl,cogvlm,instructblip,openflamingo}] [--setting {0shot,1shot}] [--context_file CONTEXT_FILE]
[--guess_if_cannot_parse]
Run different task on VIEScore.
optional arguments:
-h, --help show this help message and exit
--task {tie,mie,t2i,cig,sdig,msdig,sdie}
Select the task to run
--mllm {gpt4v,llava,blip2,fuyu,qwenvl,cogvlm,instructblip,openflamingo}
Select the MLLM model to use
--setting {0shot,1shot}
Select the incontext learning setting
--context_file CONTEXT_FILE
Which context file to use.
--guess_if_cannot_parse
Guess a value if the output cannot be parsed.
For example, you can run:
python3 run.py --task t2i --mllm gpt4v --setting 0shot --context_file context.txt
- Available context files are in
_questions
folder.
After running the experiment, you can count the results or clean it up:
python3 count_entries.py <your_answers_dir>
python3 clean_result.py <your_answers_dir>
Citation
Please kindly cite our paper if you use our code, data, models or results:
@misc{ku2023viescore,
title={VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation},
author={Max Ku and Dongfu Jiang and Cong Wei and Xiang Yue and Wenhu Chen},
year={2023},
eprint={2312.14867},
archivePrefix={arXiv},
primaryClass={cs.CV}
}