OmniCap-400M

OmniCap-400M is a large-scale, general-purpose image-text dataset containing 400 million diverse image-caption pairs collected from the open web. It is designed to support a wide range of multimodal research tasks, including vision-language pretraining, image captioning, cross-modal retrieval, and text-to-image generation.

Each entry includes rich metadata to facilitate filtering, deduplication, and analysis.

Dataset Structure

The dataset is stored in Apache Parquet format and contains the following fields:

Field	Type	Description
url	string	The source URL of the image.
md5	string	MD5 hash of the image URL (for deduplication).
width	int32	Width of the image in pixels.
height	int32	Height of the image in pixels.
blip_caption	string	Machine-generated caption using BLIP.
caption	string	Raw textual context associated with the image (e.g., alt text, surrounding HTML text).
query	string	Keywords for web search.

⚠️ Note: This dataset contains web-crawled data. Users are responsible for complying with the terms of use of the source websites and applicable laws.

Intended Use

• Pretraining or fine-tuning multimodal models (e.g., CLIP, BLIP, LLaVA).
• Training text-to-image diffusion models with improved caption quality.
• Building cross-modal search systems.
• Studying bias, safety, and robustness in large-scale vision-language data.

License

MIT License.

Citation

If you use this dataset in your research, please cite it as:

@dataset{ajax2026omnicap,
  author = {Ajax102},
  title = {OmniCap-400M: A Large-Scale General-Purpose Image-Text Dataset},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Datasets},
  url = {https://huggingface.co/datasets/Ajax102/OmniCap-400M}
}