Update README.md

README.md

@@ -32,3 +32,36 @@ model = load_model("path_to_your_model.h5")
 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           |