BLIP3o Long Caption Dataset
Dataset of 27 million images with long descriptions (~120 tokens), generated by an advanced AI model. Designed to enrich multi-modal tasks like captioning, cross-modal research, and visual reasoning.
Approximately 27 million images with captions (~120 tokens), Parquet format, 4.96 GB for the first 5 GB
Apache 2.0
Description
The dataset BLIP3o Long Caption Dataset brings together more than 27 million images, each associated with a long caption (~120 tokens) generated by an AI model such as Qwen2.5-VL-7b. It is intended to train vision-language models on complex and detailed descriptions. This corpus is particularly suited to tasks where the semantic precision of the visual description is essential.
What is this dataset for?
- Train models for the automatic generation of visual descriptions
- Pre-train multimodal systems to understand complex images
- Perform cross-modal searches (find an image from a long text, or vice versa)
Can it be enriched or improved?
Yes, this dataset can be completed with additional annotations (object types, feelings, geolocation). It is also possible to filter images by categories or complexity for specialized tasks. The integration of other languages in legends is an area for improvement for multilingual models.
🔎 In summary
🧠 Recommended for
- VLM model developers
- Multimodal AI researchers
- Smart captioning projects
🔧 Compatible tools
- Hugging Face Transformers
- BLIP2
- Qwen2
- CLIP
- LangChain multimodal
💡 Tip
To avoid over-learning, sample by visual categories or by caption length when fine-tuning.
Frequently Asked Questions
Is this dataset suitable for searching images using long text?
Yes, rich captions (~120 tokens) make it an excellent support for cross-modal research between descriptive text and visual content.
Can we filter images according to their type or content?
Yes, the dataset can be explored and filtered according to the properties of the captions (objects, scenes, etc.) using the structured format.
What is the quality of the descriptions generated?
The descriptions are generated by Qwen2.5-VL-7B, a powerful model, producing long, informative, and grammatically consistent captions.
