File size: 1,458 Bytes
42c40bc da20645 33a3af9 42c40bc 33a3af9 42c40bc 378a97f 21c9e80 42c40bc 378a97f 42c40bc 82f85c4 378a97f 82f85c4 378a97f 7c6a2c2 82f85c4 9e04a6a 82f85c4 da20645 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 |
---
base_model:
- llava-hf/llama3-llava-next-8b-hf
- openbmb/MiniCPM-V-2_6
- microsoft/Phi-3-vision-128k-instruct
- Qwen/Qwen2.5-VL-7B-Instruct
license: mit
metrics:
- accuracy
pipeline_tag: image-text-to-text
library_name: transformers
---
**The following models are obtained via supervised fine-tuning (SFT) using the ECD-10k-Images dataset ([URL](https://huggingface.co/datasets/ChartFoundation/ECD-10k-Images)) proposed in our ICCV 2025 paper, "[Effective Training Data Synthesis for Improving MLLM Chart Understanding](https://huggingface.co/papers/2508.06492)" ([Code](https://github.com/yuweiyang-anu/ECD)).**
**ECD Dataset Overview**:
**Comparing 4 MLLMs on six test sets: (CharXiv, ChartQA, ReachQA, ChartBench, ChartX, ECDBench)**
**Citation**:
If it is helpful to your research, please cite our paper as follows:
```
@inproceedings{yang2025effective,
title={Effective Training Data Synthesis for Improving MLLM Chart Understanding},
author={Yang, Yuwei and Zhang, Zeyu and Hou, Yunzhong and Li, Zhuowan and Liu, Gaowen and Payani, Ali and Ting, Yuan-Sen and Zheng, Liang},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2025}
}
``` |