Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
 license: wtfpl
+language:
+- en
+- ur
+tags:
+- facial
+- recognization
+- face
+- ML
+- Ai
 ---
+Model Card: 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.