FaceShape Model

Model Description

This model classifies facial shapes into categories such as oval, square, round, etc. It is designed for applications in virtual try-ons and eyeglass frame recommendations.

Framework: TensorFlow (Keras)
Model Format: .h5
Purpose: Face shape classification.

How to Use

To use this model, you can load it with TensorFlow and Keras. Below is an example:

from tensorflow.keras.models import load_model

# Load the model
model = load_model("path_to_your_model.h5")

# Example input
input_data = ...  # Replace with your preprocessed input
output = model.predict(input_data)
print(output)



## Training Details
The model was trained using a Convolutional Neural Network (CNN) architecture on the [Face Shape Classification Dataset](https://www.kaggle.com/datasets/lucifierx/face-shape-classification).

### Preprocessing Steps
- **Image Size**: All input images were resized to 224x224 pixels.
- **Normalization**: Pixel values were normalized to the range [0, 1].
- **Data Augmentation**: Techniques like rotation, flipping, and zooming were applied to improve generalization.

### Training Configuration
- **Framework**: TensorFlow (Keras)
- **Optimizer**: Adam
- **Loss Function**: Categorical Crossentropy
- **Batch Size**: 32
- **Epochs**: 50
- **Validation Accuracy**: Achieved 85% on the validation set.

### Hardware
The model was trained on an NVIDIA GPU for faster computation.

## Limitations
- The model may not perform well with low-resolution or occluded images.
- The dataset may not represent all possible face shapes, which could limit generalization.

## Example Predictions
Here are some example predictions:

| Input Image       | Predicted Class |
|--------------------|-----------------|
| ![example1](https://via.placeholder.com/100) | Oval             |
| ![example2](https://via.placeholder.com/100) | Square           |