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--- |
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title: Aesthetix AI |
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emoji: 🗿 |
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colorFrom: gray |
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colorTo: purple |
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sdk: streamlit |
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sdk_version: 1.29.0 |
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app_file: app.py |
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pinned: false |
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--- |
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# Aesthetix AI: Facial Symmetry & Aesthetic Rater 🗿 |
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An AI-powered computer vision system that analyzes facial aesthetics and predicts a rating on a 1.0-5.0 scale. Built with PyTorch, utilizing a fine-tuned ResNet18 architecture and Grad-CAM for visual explainability. |
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**Try the App:** [Link to your Hugging Face Space] |
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## Overview |
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Unlike standard face classifiers that just detect identity, Aesthetix AI is a **regression model** trained to quantify subjective facial attractiveness based on the SCUT-FBP5500 Dataset. |
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It features a complete inference pipeline: |
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1. **Face Isolation**: Uses Haar Cascades to detect and tightly crop the face. |
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2. **Semantic Segmentation**: Uses DeepLabV3 to remove background noise (hair/neck masking) to force the model to evaluate facial geometry only. |
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3. **Scoring Engine**: A ResNet18 CNN fine-tuned to predict a continuous float score. |
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4. **Explainability**: Generates Grad-CAM heatmaps to visualize exactly which features (eyes, jawline, symmetry) the model focused on. |
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## Performance |
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- **Architecture**: ResNet18 (Pre-trained on ImageNet → Fine-tuned) |
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- **Loss Function**: MSELoss (Mean Squared Error) |
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- **Optimizer**: Adam (lr=1e-4) |
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- **Validation Loss**: 0.0858 (MSE) |
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- **Interpretation**: The model's predictions are on average within +/- 0.29 points of the human ground truth. |
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## The Stack |
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- **PyTorch**: Core deep learning framework. |
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- **Torchvision**: Pre-trained models (ResNet18, DeepLabV3). |
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- **OpenCV**: Face detection and image processing. |
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- **Streamlit**: Interactive web interface. |
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- **Grad-CAM**: Visual attention mapping. |
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## Installation & Usage |
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1. Clone the repo: |
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```bash |
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git clone https://github.com/AKMessi/facial-rating-using-cnn.git |
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cd facial-beauty-rating-cnns# facial-rating-using-cnn |
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