README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: response
      dtype: string
  splits:
    - name: train
      num_bytes: 852338950
      num_examples: 10000
  download_size: 1335123428
  dataset_size: 852338950
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
tags:
  - deep-caption
  - coco
license: apache-2.0
task_categories:
  - image-to-text
  - image-text-to-text
language:
  - en
pretty_name: OpenCaption
size_categories:
  - 1K<n<10K

OpenCaption-Unified-10K

OpenCaption-Unified-10K is a dense image captioning dataset built from 10,000 images paired with long-form synthetic captions generated using the Qwen3.5 multimodal model. Each caption is produced through a dedicated Qwen3.5 captioning pipeline designed to generate detailed, high-fidelity descriptions of scene composition, subject attributes, spatial relationships, activities, and overall visual context rather than short, generic captions. The dataset is intended for training and evaluating image-to-text, vision-language, and dense captioning models that require rich textual grounding. The dataset is built primarily from publicly available images, which make up the majority of the input imagery, together with additional publicly available datasets. Every image is paired with a single comprehensive caption that attempts to describe the complete visual scene, making the dataset suitable for supervised vision-language training, instruction tuning, caption refinement, retrieval, and multimodal research.

Dataset Statistics

Property	Value
Number of Samples	10,000
Annotation Type	Long-form Dense Caption
Caption Generator	Qwen3.5 Multimodal
Dataset Format	Optimized Parquet

Dataset Structure

Each sample contains the following fields:

Column	Type	Description
image	Image	Original input image
response	String	Long-form dense caption describing the image

Example:

sample = ds[0]

print(sample.keys())

# dict_keys([
#     "image",
#     "response"
# ])

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset(
    "prithivMLmods/OpenCaption-Unified-10K",
    split="train"
)

Example Usage

from datasets import load_dataset
import matplotlib.pyplot as plt

dataset = load_dataset(
    "prithivMLmods/OpenCaption-Unified-10K",
    split="train"
)

sample = dataset[0]

image = sample["image"]
caption = sample["response"]

print(caption)

plt.figure(figsize=(8, 8))
plt.imshow(image)
plt.axis("off")
plt.show()

Citation

@misc{prithiv_sakthi_2026,
    author       = { Prithiv Sakthi },
    title        = { OpenCaption-Unified-10K (Revision 75cba9f) },
    year         = 2026,
    url          = { https://huggingface.co/datasets/prithivMLmods/OpenCaption-Unified-10K },
    doi          = { 10.57967/hf/9578 },
    publisher    = { Hugging Face }
}

License

This dataset is released under the Apache-2.0 License.