LIMO_VQA: A Visual Question Answering Dataset

LIMO_VQA is a dataset for Visual Question Answering (VQA) tasks, likely containing pairs of images and associated questions. The dataset is hosted on Kaggle, a popular platform for data science competitions and projects. Specific details on its size, creation date, and authors are not provided in the available metadata.

Use Cases

• Train a model to answer questions about image content (inferred from domain, verify after download)
• Benchmark the performance of vision-language models on VQA tasks (inferred from domain, verify after download)
• Fine-tune a pre-trained multimodal LLM for specific visual reasoning applications (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a platform with established data sharing and versioning infrastructure.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count, column definitions, and sample data are unknown, which may limit suitability assessment.
• License, author, and last update date are unknown, affecting reproducibility and usage rights verification.

Provenance

Source

Kaggle