# Vis-Edit Instruct Dataset

**Vis-Edit Instruct** is a custom-built benchmark dataset designed to evaluate the performance of image editing models in a variety of real-world scenarios. Despite its small size, this dataset is diverse and curated to represent the complex and practical use cases encountered in image editing tasks.

## 📦 Dataset Overview

- **Total Images**: 35
- **Total Editing Requests**: 90
- **Main Purpose**: Benchmarking and evaluating the capabilities of image editing models under diverse and challenging conditions.

## 🖼️ Image Categories

The dataset includes 35 carefully selected images sourced from various online platforms. These images are distributed across five content categories to ensure a rich variety of contexts:

1. **People**
   - Includes full-body and portrait shots
   - Captures real-life activities such as playing sports, riding motorcycles, and posing
   - Helps assess the model's understanding of human features and body language

2. **Natural Landscapes**
   - Features forests, beaches, oceans, and rivers
   - Captured under different lighting conditions and textures
   - Tests object recognition and scene composition skills

3. **Animals**
   - Contains dogs, cats, rabbits, elephants, and more
   - Includes various poses such as sitting and running
   - Evaluates model performance in animal-related edits

4. **Flowers and Plants**
   - Diverse flora types with intricate textures and vibrant colors
   - Designed to test the model's fine-grained recognition ability

5. **Urban Contexts**
   - Scenes include fashion boutiques, cafes, residential buildings, and street views
   - Designed to assess the model’s capability in handling complex urban environments

Each category is carefully balanced to ensure comprehensive representation of real-world scenarios.

## 🧪 Editing Request Scenarios

To assess editing models effectively, **Vis-Edit Instruct** includes 90 editing instructions categorized into six key scenarios (15 instructions per scenario):

1. **Sufficient Information**
   - Requests provide clear and complete details for straightforward execution

2. **Lack of Interactive Object**
   - The object to be modified is missing or not clearly identified

3. **Ambiguous Interactive Object**
   - The targeted object appears multiple times, creating uncertainty

4. **Unsupported Tasks**
   - Instructions include tasks that go beyond current model capabilities

5. **Insufficient Specific Parameters**
   - Lacks precise instructions, emphasizing the need for detailed user input

6. **Parameter Suggestions**
   - Requests come with user-suggested parameters, testing compliance and precision

These scenarios reflect real user interactions and serve as valuable challenges for evaluating and improving image editing systems.

## 📊 Category Distribution

A distribution chart is provided in the dataset (`category_distribution.png`) to visualize the representation of each category.

## 🔍 Applications

This dataset is ideal for:
- Evaluating multimodal image editing models
- Benchmarking instruction-following capabilities
- Testing robustness across ambiguous or underspecified prompts
- Enhancing adaptability in real-world image editing scenarios

## 📜 License

This dataset is released for academic research purposes. Please ensure proper citation when used.

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For more details, example images, and baseline results, please refer to the documentation and sample folder provided in this repository.