README.md

---
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:

```python
sample = ds[0]

print(sample.keys())

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

## Loading the Dataset

```python
from datasets import load_dataset

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

## Example Usage

```python
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

```bibtex
@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**.