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--- |
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library_name: keras |
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tags: |
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- tensorflow |
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- keras |
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- face-shape-classification |
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- cnn |
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pipeline_tag: image-classification |
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license: apache-2.0 |
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--- |
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# FaceShape Model |
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## Model Description |
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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. |
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- **Framework:** TensorFlow (Keras) |
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- **Model Format:** `.h5` |
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- **Purpose:** Face shape classification. |
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## How to Use |
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To use this model, you can load it with TensorFlow and Keras. Below is an example: |
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```python |
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from tensorflow.keras.models import load_model |
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# Load the model |
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model = load_model("path_to_your_model.h5") |
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# Example input |
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input_data = ... # Replace with your preprocessed input |
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output = model.predict(input_data) |
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print(output) |
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## Training Details |
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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). |
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### Preprocessing Steps |
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- **Image Size**: All input images were resized to 224x224 pixels. |
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- **Normalization**: Pixel values were normalized to the range [0, 1]. |
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- **Data Augmentation**: Techniques like rotation, flipping, and zooming were applied to improve generalization. |
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### Training Configuration |
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- **Framework**: TensorFlow (Keras) |
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- **Optimizer**: Adam |
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- **Loss Function**: Categorical Crossentropy |
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- **Batch Size**: 32 |
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- **Epochs**: 50 |
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- **Validation Accuracy**: Achieved 85% on the validation set. |
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### Hardware |
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The model was trained on an NVIDIA GPU for faster computation. |
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## Limitations |
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- The model may not perform well with low-resolution or occluded images. |
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- The dataset may not represent all possible face shapes, which could limit generalization. |
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## Example Predictions |
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Here are some example predictions: |
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| Input Image | Predicted Class | |
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|--------------------|-----------------| |
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|  | Oval | |
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|  | Square | |
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