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--- |
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title: CIET |
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emoji: 👀 |
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colorFrom: yellow |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 5.18.0 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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short_description: Comprehensive Image Evaluation Tool |
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--- |
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# Comprehensive Image Evaluation Tool |
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This tool combines multiple image evaluation models into a single application with a user-friendly interface for analyzing and reviewing images. |
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## Features |
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- **Batch Processing**: Upload multiple images at once for efficient evaluation |
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- **Multiple Models**: Combines evaluations from several aesthetic prediction models: |
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- ShadowLilac's aesthetic-shadow-v2 |
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- WaifuScorer V4 |
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- CafeAI's aesthetic, style and waifu classifiers |
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- Anime Aesthetic predictor |
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- **Comprehensive Analysis**: Get detailed metrics for each image |
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- **Results Table**: View results sorted by score with image previews |
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- **Export**: Save results to CSV for further analysis |
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- **Single Image Mode**: Evaluate individual images and get detailed results |
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## Installation |
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1. Clone this repository: |
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``` |
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git clone [repository-url] |
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cd image-evaluation-tool |
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``` |
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2. Install required dependencies: |
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``` |
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pip install -r requirements.txt |
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``` |
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3. Run the application: |
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``` |
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python app.py |
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``` |
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## Usage |
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### Batch Processing |
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1. Launch the application |
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2. Use the file upload panel to select multiple images |
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3. Adjust the HQ threshold if needed (default 0.5) |
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4. Click "Process Images" |
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5. View results in the table sorted by average score |
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6. Click "Export Results to CSV" to save the data |
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### Single Image Evaluation |
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1. Scroll down to the Single Image Evaluation section |
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2. Upload an image |
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3. Click "Evaluate" |
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4. View detailed metrics and style information |
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## Models Information |
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- **ShadowLilac** (0-1): General aesthetic quality assessment |
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- **WaifuScorer** (0-10): Specialized for anime-style images |
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- **CafeAI** (0-1): Style classification and aesthetic assessment |
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- **Anime Aesthetic** (0-10): Specialized for anime/manga art |
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## Output Folders |
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- `output/hq_folder`: Images that meet or exceed the threshold |
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- `output/lq_folder`: Images that score below the threshold |
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## Requirements |
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- Python 3.8+ |
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- CUDA-compatible GPU recommended for faster processing |
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- ~4GB of disk space for model downloads (first run) |
