The Cauldron
An open-source data set containing 50 vision-language datasets, designed to train multimodal models like Idefics2.
Description
The Cauldron is a vast aggregate of 50 vision-language datasets, used for fine-tuning the Idefics2 model. It includes only the training sets, in a unified format (Parquet), combining images and descriptive texts. The game has been carefully “decontaminated” by removing examples that cross the MMMU, MathVista, and MMBench benchmarks.
What is this dataset for?
- Train multimodal models (VLMs) on a wide variety of image/text tasks
- Improve performance on tasks such as captioning, VQA, OCR, or visual reasoning
- Establishing a robust basis for fine-tuning or pre-training open-source architectures
Can it be enriched or improved?
Yes. It is possible to complete this dataset with additional annotations (e.g. difficulty levels, languages, scene types) or to add image/text enhancement modules to diversify the entries. It can also be filtered to build thematic sub-corpora.
🔎 In summary
🧠 Recommended for
- VQA model developers
- Multimodal AI researchers
- R&D teams in NLP+Vision
🔧 Compatible tools
- Transformers
- PyTorch
- Jupyter
- VLLM
- Hugging Face Datasets
💡 Tip
Filter the subsets most suited to your task (captioning, QA, OCR...) to reduce the need for training resources.
Frequently Asked Questions
What types of tasks can you train with this dataset?
It allows you to train models for captioning, VQA, OCR, visual reasoning, and any task combining image and text.
Is it possible to select a subset of the dataset?
Yes, since the dataset is an aggregation of 50 sources, it is easy to filter some of them according to the needs of a specific project.
Is it suitable for training limited GPU models?
By filtering and reducing image sizes and textual contexts, partial exploitation is possible on more modest machines.
