README.md

metadata

title: Aesthetix AI
emoji: 🗿
colorFrom: gray
colorTo: purple
sdk: streamlit
sdk_version: 1.29.0
app_file: app.py
pinned: false

Aesthetix AI: Facial Symmetry & Aesthetic Rater 🗿

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.

Try the App: [Link to your Hugging Face Space]

Overview

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.

It features a complete inference pipeline:

1. Face Isolation: Uses Haar Cascades to detect and tightly crop the face.
2. Semantic Segmentation: Uses DeepLabV3 to remove background noise (hair/neck masking) to force the model to evaluate facial geometry only.
3. Scoring Engine: A ResNet18 CNN fine-tuned to predict a continuous float score.
4. Explainability: Generates Grad-CAM heatmaps to visualize exactly which features (eyes, jawline, symmetry) the model focused on.

Performance

• Architecture: ResNet18 (Pre-trained on ImageNet → Fine-tuned)
• Loss Function: MSELoss (Mean Squared Error)
• Optimizer: Adam (lr=1e-4)
• Validation Loss: 0.0858 (MSE)
  • Interpretation: The model's predictions are on average within +/- 0.29 points of the human ground truth.

The Stack

• PyTorch: Core deep learning framework.
• Torchvision: Pre-trained models (ResNet18, DeepLabV3).
• OpenCV: Face detection and image processing.
• Streamlit: Interactive web interface.
• Grad-CAM: Visual attention mapping.

Installation & Usage

1. Clone the repo:

   git clone https://github.com/AKMessi/facial-rating-using-cnn.git
   cd facial-beauty-rating-cnns# facial-rating-using-cnn