Omkar2118/datasets huggingface dataset:
RetVQA
Full name: Retrieval-Based Visual Question Answering

The RetVQA dataset is a large-scale dataset designed for Retrieval-Based Visual Question Answering (RetVQA). RetVQA is a more challenging task than traditional VQA, as it requires models to retrieve relevant images from a pool of images before answering a question. The need for RetVQA stems from the fact that information needed to answer a question may be spread across multiple images. 

Here is a detailed summary of the RetVQA dataset:

* It is **20 times larger** than the closest dataset in this setting, WebQA.
* It was derived from the Visual Genome dataset, utilising its questions and annotations of images.
* It has **418K unique questions** and **16,205 unique precise answers**.
*  The questions are designed to be **metadata-independent**, meaning they do not rely on information such as captions or tags.
* The questions are divided into five categories: 
    * **color** 
    * **shape**
    * **count**
    * **object-attributes**
    * **relation-based**.
* The dataset includes both **binary (yes/no)** questions and **open-ended questions** that require a generative answer.
* All answers are free-form and fluent, even for binary questions. For example, a binary question may be "Do the rose and sunflower share the same colour?", and a corresponding answer would be "No, the rose and sunflower do not share the same colour".
*  Every question in RetVQA requires reasoning over **multiple images** to arrive at the answer. This contrasts with datasets like WebQA, where a majority of questions can be answered using a single image.
* The dataset has, on average, **two relevant images and 24.5 irrelevant images per question**. This makes it more challenging than datasets like ISVQA, where images are homogeneous and no explicit retrieval is needed.

Introducing paper: Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering