UVT Explanatory Based Vision Tasks
Large-scale dataset combining images, explanatory instructions in natural language, and corresponding outputs for advanced computer vision tasks.
Approximately 284,000 image-instruction-output triples, 5.5 GB (Parquet format)
MIT
Description
The dataset UVT Explanatory Based Vision Tasks offers more than 280,000 triples composed of images, explanatory instructions in natural language, and expected outputs. It aims to improve the understanding and generalization of zero-shot models in computer vision.
What is this dataset for?
- Train vision models that can understand and follow complex instructions
- Improve zero-shot generalization on various vision tasks
- Seek unified approaches to vision tasks and their interpretation
Can it be enriched or improved?
Yes, it is possible to add additional annotations, expand the corpus with new tasks, or customize instructions for specific applications.
🔎 In summary
🧠 Recommended for
- Computer vision researchers
- Multi-tasking model developers
- Zero-shot learning specialists
🔧 Compatible tools
- PyTorch
- TensorFlow
- Transformers
- Datasets Parquet
💡 Tip
Use explanatory instructions to improve the ability of models to understand complex tasks without specific annotations.
Frequently Asked Questions
Does this dataset only contain images or also text annotations?
It contains images as well as explanatory text instructions and associated outputs.
Can this dataset be used to train zero-shot vision models?
Yes, that is precisely its main objective: to improve zero-shot generalization.
What is the approximate size and format of the data?
Approximately 284,000 triplets, 5.5 GB in Parquet format, adapted for efficient batch processing.
