| # Vis-Edit Instruct Dataset |
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| **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. |
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| ## 📦 Dataset Overview |
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| - **Total Images**: 35 |
| - **Total Editing Requests**: 90 |
| - **Main Purpose**: Benchmarking and evaluating the capabilities of image editing models under diverse and challenging conditions. |
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| ## 🖼️ Image Categories |
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| 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: |
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| 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 |
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| 2. **Natural Landscapes** |
| - Features forests, beaches, oceans, and rivers |
| - Captured under different lighting conditions and textures |
| - Tests object recognition and scene composition skills |
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| 3. **Animals** |
| - Contains dogs, cats, rabbits, elephants, and more |
| - Includes various poses such as sitting and running |
| - Evaluates model performance in animal-related edits |
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| 4. **Flowers and Plants** |
| - Diverse flora types with intricate textures and vibrant colors |
| - Designed to test the model's fine-grained recognition ability |
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| 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 |
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| Each category is carefully balanced to ensure comprehensive representation of real-world scenarios. |
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| ## 🧪 Editing Request Scenarios |
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| To assess editing models effectively, **Vis-Edit Instruct** includes 90 editing instructions categorized into six key scenarios (15 instructions per scenario): |
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| 1. **Sufficient Information** |
| - Requests provide clear and complete details for straightforward execution |
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| 2. **Lack of Interactive Object** |
| - The object to be modified is missing or not clearly identified |
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| 3. **Ambiguous Interactive Object** |
| - The targeted object appears multiple times, creating uncertainty |
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| 4. **Unsupported Tasks** |
| - Instructions include tasks that go beyond current model capabilities |
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| 5. **Insufficient Specific Parameters** |
| - Lacks precise instructions, emphasizing the need for detailed user input |
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| 6. **Parameter Suggestions** |
| - Requests come with user-suggested parameters, testing compliance and precision |
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| These scenarios reflect real user interactions and serve as valuable challenges for evaluating and improving image editing systems. |
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| ## 📊 Category Distribution |
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| A distribution chart is provided in the dataset (`category_distribution.png`) to visualize the representation of each category. |
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| ## 🔍 Applications |
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| 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 |
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| ## 📜 License |
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| 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. |
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