Facial Recognition Model
Model Information
Name: Facial Means
Type: Convolutional Neural Network (CNN)
Framework: TensorFlow
Dataset: Celebrity Faces Dataset
Dataset Path: /content/drive/MyDrive/beard_dataset/celb_dataset/
Model Save Path: /content/drive/MyDrive/beard_dataset/celebrity_model.h5
Image Dimensions: 224 x 224 pixels
Batch Size: 32 Data Augmentation Rescale: 1./255 Shear Range: 0.2 Zoom Range: 0.2 Horizontal Flip: True Validation Split: 20% Model Architecture Layer (type) Output Shape Param # =============================================================== conv2d (Conv2D) (None, 222, 222, 32) 896 max_pooling2d (MaxPooling2D) (None, 111, 111, 32) 0 conv2d_1 (Conv2D) (None, 109, 109, 64) 18496 max_pooling2d_1 (MaxPooling2D)(None, 54, 54, 64) 0 conv2d_2 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2 (MaxPooling2D)(None, 26, 26, 128) 0 flatten (Flatten) (None, 86528) 0 dense (Dense) (None, 128) 11075712 dense_1 (Dense) (None, 6) 774 ===============================================================
Total params: 11,170,734 Trainable params: 11,170,734 Non-trainable params: 0 Model Compilation Optimizer: Adam Loss Function: Categorical Crossentropy Metrics: Accuracy
Training
Epochs: 10
Steps per Epoch: Calculated based on the training dataset size and batch size. Validation Steps: Calculated based on the validation dataset size and batch size. Model Save The trained model is saved at /content/drive/MyDrive/beard_dataset/celebrity_model.h5. Conclusion The facial recognition model has been trained on a celebrity faces dataset using TensorFlow. The model architecture includes convolutional and pooling layers, followed by fully connected layers for classification. The training process involves data augmentation and achieves satisfactory accuracy.
Model training code provided by Muhammad Sajjad Rasool.
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