---
tags:
- image-to-text
- visual-question-answering
- image-captioning
datasets:
- kaist-ai/volcano-train
language:
- en
pipeline_tag: image-to-text
library_name: transformers
---
## Links for Reference

- **Repository: https://github.com/kaistAI/Volcano** 
- **Paper: https://arxiv.org/abs/2311.07362** 
  
# Overview
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6550c4f27bbfce1878f5f280/AnqbCNf6pRiQ_5uNX0r4d.png)
Volcano employs a single LMM to generate initial responses, feedback, and revisions, as well as decisions to accept revisions. It follows a sequential procedure of an iterative critique-revision-decide loop.

# Model details

**Model type:**
Volcano-7b is a multimodal self-feedback guided revision model that was fine-tuned by mixing the visual instruction tuning dataset used in [LLaVA-v1.5](https://llava-vl.github.io/) with multimodal feedback and revision data collected through [gpt-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5), applied to the [vicuna-7b-v1.5](https://huggingface.co./lmsys/vicuna-7b-v1.5) model.

**Model date:**
Volcano-7b was trained in October 2023.

# Training dataset
- **274K multimodal feedback and revision data**
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data

You can find [here](https://huggingface.co./datasets/kaist-ai/volcano-train) the dataset used to train Volcano, which includes all the aforementioned datasets.

# Evaluation dataset
A collection of three multimodal hallucination benchmarks ([MMHal-Bench](https://huggingface.co./datasets/Shengcao1006/MMHal-Bench), [Pope](https://github.com/RUCAIBox/POPE), [GAVIE](https://github.com/FuxiaoLiu/LRV-Instruction)) and two multimodal understanding benchmarks ([MM-Vet](https://github.com/yuweihao/MM-Vet), [MMBench](https://github.com/open-compass/MMBench)).