---
license: mit
pipeline_tag: image-to-text
library_name: transformers
tags:
  - chart-captioning
  - multimodal
  - vision-language-model
---

# ChartCap: Mitigating Hallucination of Dense Chart Captioning

This repository contains the model presented in the paper [**ChartCap: Mitigating Hallucination of Dense Chart Captioning**](https://huggingface.co/papers/2508.03164).

**Project Page:** (WIP) [https://junyoung-00.github.io/ChartCap/](https://junyoung-00.github.io/ChartCap/)\
**Code:** [https://github.com/junyoung-00/ChartCap](https://github.com/junyoung-00/ChartCap)

## Model Description

`Phi-3.5-vision-instruct-ChartCap` is a ChartCap-fine-tuned version of microsoft/Phi-3.5-vision-instruct.

The model aims to generate high-quality, dense captions for charts, ensuring that the generated text accurately captures structural elements and key insights discernible from the charts, while mitigating the inclusion of extraneous or hallucinated information.

## How to Use

```python
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import requests
import torch

model_id = "junyoung-00/Phi-3.5-vision-instruct-ChartCap" 

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

# Load an example chart image (URL or local path)
image_url = "https://your-server.com/example_chart.png"
image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")

# Define the prompt for dense chart captioning
prompt = "Please provide a detailed caption for the chart."
messages = [
    {"role": "user", "content": f"<|image|>
{prompt}"}
]

# Apply chat template and prepare inputs
input_ids = processor.tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
# The image token handling for Phi3V can sometimes be specific, ensure correct placeholder handling if <|image|> is mapped.
# For simplicity, we use the standard processor input which handles image embedding.
inputs = processor(text=input_ids, images=image, return_tensors="pt").to(model.device)


# Generate response
generated_ids = model.generate(**inputs, max_new_tokens=512)

# Decode and print the output
response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response.strip())
```

## Citation

If you find this model or the associated research helpful, please cite:

```bibtex
@inproceedings{{lim2025chartcap,
  title={{ChartCap: Mitigating Hallucination of Dense Chart Captioning}},
  author={{Junyoung Lim and Jaewoo Ahn and Gunhee Kim}},
  booktitle={{Proceedings of the IEEE/CVF International Conference on Computer Vision}},
  year={{2025}}
}}
```