Datasets:
File size: 3,067 Bytes
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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**. |