MMPR v1.2
Multimodal dataset designed to train models to improve their reasoning skills and comprehension in complex vision and language tasks.
Multimodal dataset (text and images), large volume used for several benchmarks (VQA, MathVision, POPE)
MIT
Description
The dataset MMPR v1.2 is a multimodal corpus comprising text and image data, used for fine-tuning models for complex tasks such as multimodal reasoning and visual comprehension. It has made it possible to train models achieving peak performance on several recognized benchmarks.
What is this dataset for?
- Improving visual comprehension combined with the reasoning of multimodal models
- Train models for complex Visual Question Answering (VQA) tasks
- Testing the robustness of models in the face of visual hallucinations
Can it be enriched or improved?
This dataset can be supplemented by additional annotations on the images, or by multimodal data in other languages or contexts. Integrating more varied examples can also increase the diversity and robustness of models.
🔎 In summary
🧠 Recommended for
- Vision-language researchers
- Multimodal AI teams
- VQA developers
🔧 Compatible tools
- Hugging Face Transformers
- MMF
- Detectron2
- PyTorch
💡 Tip
Verify the quality of alignment between images and texts to maximize the quality of training.
Frequently Asked Questions
Is this dataset suitable for text-only models?
No, it is specifically designed for multimodal models combining image and text.
What is the approximate size of the dataset?
The dataset is large and used on several benchmarks, but the exact size is not publicly specified.
Can this dataset be used to reduce hallucinations in models?
Yes, it includes data to improve robustness and reduce hallucinations in multimodal models.
