Datasetpublic

prithivMLmods/MultiImage-Caption

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.… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/MultiImage-Caption.

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Dataset Card

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
—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}}
}