Datasets:

prithivMLmods
/

MultiImage-Caption

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
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

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`

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

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

@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}}
}