daoddp2306/Vis-Edit_Instruct_Dataset

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.