Omkar2118/datasets huggingface dataset:
IllusionMNIST_test

## IllusionMNIST_test  

### Dataset Characteristics  
IllusionMNIST_test is a generated dataset derived from the MNIST dataset. It introduces a novel element of *pareidolia*—a phenomenon where patterns, often faces, are perceived in random or abstract stimuli. The dataset contains 11 classes: the original 10 digits from MNIST, and an additional "No Illusion" class. It includes 1,219 samples, all synthetically created rather than real-world images.  

### Motivations and Content Summary  
The dataset was created using ControlNet for image generation, with captions produced by four large language models (LLMs). The goal is to introduce visual illusions and pareidolia into an otherwise familiar domain, encouraging models to handle the nuanced challenge of reasoning about visual illusions. This expands upon the MNIST dataset by blending the simplicity of handwritten digits with the complexity of human perception.  

### Potential Use Cases  
- **Illusory VQA:** Questioning models about the illusions present in the images.  
- **Multimodal Model Evaluation:** Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns.  
- **Perceptual Studies:** Understanding how AI models perceive and classify pareidolia, a phenomenon deeply tied to human visual perception.  
- **Synthetic Data Research:** Exploring the use of generated datasets to introduce unconventional challenges to machine learning models.

Introducing paper: Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
IllusionFashionMNIST_test

## IllusionFashionMNIST_test  

### Dataset Characteristics  
IllusionFashionMNIST_test is a generated dataset derived from the FashionMNIST dataset. It incorporates the concept of *pareidolia*—a phenomenon where patterns, often faces, are perceived in random or abstract stimuli. The dataset contains 11 classes: the original 10 classes from FashionMNIST, and an additional "No Illusion" class. It includes 1,267 samples, all synthetically created rather than real-world images.  

### Motivations and Content Summary  
The dataset was created using ControlNet for image generation, with captions produced by four large language models (LLMs). The aim is to combine the complex visual patterns in fashion items with the phenomenon of pareidolia, encouraging models to reason about visual illusions in the context of non-digit, real-world-like data. This introduces an added layer of abstraction to the already challenging task of image classification.  

### Potential Use Cases  
- **Illusory VQA:** Questioning models about the illusions present in the images.  
- **Multimodal Model Evaluation:** Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns.  
- **Perceptual Studies:** Investigating how AI models perceive pareidolia in fashion-related visuals.  
- **Synthetic Data Research:** Exploring the use of generated datasets to present unconventional challenges to machine learning models.

Introducing paper: Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
IllusionAnimals_test

## IllusionAnimals_test  

### Dataset Characteristics  
IllusionAnimals_test is a generated dataset based on a synthetic collection of animal images, including 10 animal classes: *cat*, *dog*, *pigeon*, *butterfly*, *elephant*, *horse*, *deer*, *snake*, *fish*, and *rooster*. Additionally, it includes a "No Illusion" class, bringing the total number of classes to 11. The dataset contains 1,100 samples, all created synthetically rather than derived from real-world images.  

### Motivations and Content Summary  
The dataset was designed using ControlNet for image generation, with captions provided by four large language models (LLMs). Its purpose is to incorporate the phenomenon of *pareidolia*—where patterns, often faces, are perceived in random or abstract stimuli—into a variety of animal-related visual contexts. By focusing on animal images, this dataset broadens the scope of illusory reasoning tasks to include more naturalistic and complex visual patterns.  

### Potential Use Cases  
- **Illusory VQA:** Questioning models about the illusions present in the images.  
- **Multimodal Model Evaluation:** Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns in naturalistic images.  
- **Perceptual Studies:** Exploring how AI models perceive pareidolia in animal-related visual data.  
- **Synthetic Data Research:** Investigating the use of generated datasets to challenge machine learning models with abstract and complex patterns.

Introducing paper: Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
IllusionChar_test

## IllusionChar_test  

### Dataset Characteristics  
IllusionChar_test is a generated dataset containing 3,300 samples of images that feature sequences of 3 to 5 random characters. Unlike classification-focused datasets, this dataset is designed for tasks that require reasoning about patterns, sequences, or illusions within the character sequences. All images are synthetically generated, and no real-world data is included.  

### Motivations and Content Summary  
The dataset was created using ControlNet for generating images and captions from four large language models (LLMs). It aims to incorporate the phenomenon of *pareidolia*, encouraging models to discern illusions or patterns within character sequences. By focusing on character-based sequences rather than classification, this dataset challenges multimodal models to analyze abstract combinations of symbols and interpret any illusory aspects.  

### Potential Use Cases  
- **Illusory VQA:** Questioning models about potential illusions or patterns in the character sequences.  
- **Sequence Reasoning Tasks:** Evaluating a model’s ability to interpret and reason about ordered sequences.  
- **Multimodal Model Evaluation:** Benchmarking models on abstract and symbolic data with potential illusions.  
- **Synthetic Data Research:** Exploring synthetic datasets for challenging machine learning models with abstract reasoning tasks.

Introducing paper: Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions