---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    list: image
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 4444062367
    num_examples: 10602
  download_size: 4431804584
  dataset_size: 4444062367
task_categories:
- image-text-to-text
language:
- en
pretty_name: MultiImage
size_categories:
- 10K<n<100K
tags:
- image-captioning
- dense-captioning
- computer-vision
- multimodal
- synthetic-captions
- deep-caption
- image
---

# MultiImage-Caption

**MultiImage-Caption** is a multimodal dense captioning dataset featuring **10,602** entries designed for training, supervised fine-tuning (SFT), and evaluating multi-image Vision-Language Models (such as Qwen2-VL, LLaVA-NeXT, and PaliGemma). 

Each sample pairs an interleaved set of multiple images (`images list`) with comprehensive, comparative, and granular descriptive captions (`response`) analyzing the context, subjects, and interactions across all provided images.

- **Curator:** [prithivMLmods](https://huggingface.co/prithivMLmods)
- **Total Samples:** 10,602 rows
- **Total Size:** ~4.43 GB
- **Format:** Parquet (`image`, `response`)
- **Modalities:** Image, Text
- **Split:** Train

## Dataset Structure & Schema

### Feature Fields

| Field | Type | Description |
| :--- | :--- | :--- |
| `image` | `Sequence[Image]` | A list of multiple target RGB images grouped together per entry |
| `response` | `string` | Dense, structured textual explanation breaking down and comparing each individual image |

### Example Response Structure

```text
Here is a detailed explanation of each image:

**Image 1**: This photograph captures an energetic moment at an outdoor event...
**Image 2**: A contrasting perspective showing subjects interacting with their surroundings...
```

## How to Use

### Loading with `datasets`

```python
from datasets import load_dataset

# Load dataset from the Hugging Face Hub
dataset = load_dataset("prithivMLmods/MultiImage-Caption", split="train")

# Access a single sample
sample = dataset[0]
images = sample["image"]        # List of PIL Images
response = sample["response"]   # Multi-image detailed caption

print(f"Number of images in sample: {len(images)}")
print("Response preview:\n", response[:250])
```

### Formatting for Multi-Image VLM SFT

```python
def format_multi_image_conversation(example):
    num_images = len(example["image"])
    image_tokens = "".join([f"<image_{i+1}>\n" for i in range(num_images)])
    
    prompt = (
        f"{image_tokens}Provide a detailed, step-by-step description and comparative "
        "analysis of each of the provided images."
    )
    
    return {
        "images": example["image"],
        "prompt": prompt,
        "completion": example["response"]
    }

```

## Intended Uses

* **Multi-Image Reasoning:** Training models to correlate, compare, and reason over sequences of visual inputs simultaneously.
* **Dense Captioning:** Generating rich, descriptive long-form visual commentary instead of brief single-sentence captions.
* **Interleaved Multimodal Instruction-Tuning:** Building datasets for conversational agents handling multi-image document analysis, video keyframes, or side-by-side visual comparisons.

## License

This dataset is distributed under the **Apache-2.0 License**.

## Citation

```bibtex
@misc{prithivmlmods2026multiimagecaption,
  title        = {MultiImage-Caption: A Dense Multi-Image Multimodal Dataset},
  author       = {prithivMLmods},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/prithivMLmods/MultiImage-Caption}}
}
```