GPT-4V Multimodal Dataset
Multimodal dataset composed of more than 12,000 images associated with detailed captions. Suitable for text generation tasks from images or vice versa.
Description
The dataset GPT-4V Multimodal contains over 12,000 examples combining an image and a rich text description. Each line includes a URL to the image as well as an illustrative caption, making it an ideal base for projects that intersect vision and language.
What is this dataset for?
- Train models for the automatic generation of image captures
- Test multimodal architectures like GPT-4V, BLIP, or Flamingo
- Evaluate the ability of a model to link visual description and text
Can it be enriched or improved?
Yes, the dataset can be enriched with metadata (location, style, detected objects) or translated to create a multilingual corpus. It is also possible to annotate the semantic complexity or the ambiguity of the captions for more advanced uses.
🔎 In summary
🧠 Recommended for
- Beginners in vision-language
- Captioning projects
- Tests with visual LLM
🔧 Compatible tools
- BLIP
- GPT-4V
- CLIP
- Hugging Face Transformers
- OpenClip
💡 Tip
For quick testing, extract a subset that is balanced between object types and image styles.
Frequently Asked Questions
Does this dataset contain the images themselves?
No, it contains links to the images, which you can download if needed for local use.
Is the text generated or annotated manually?
Captions are generated automatically, but with a detailed and relevant description for each image.
Can this dataset be used for automatic image translation?
Indirectly yes: the captions can be translated to constitute a corpus of multilingual image ↔ text alignment.
