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Facial_Age_and_Gender_Detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 29 15:05:30 2024
+
+@author: Dhrumit Patel
+"""
+
+"""
+Dataset: UTKFace
+https://www.kaggle.com/datasets/jangedoo/utkface-new?resource=download
+"""
+
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import os
+import matplotlib.pyplot as plt
+import cv2
+from keras.models import Sequential, Model, load_model
+from keras.layers import Conv2D, MaxPool2D, Dense, Dropout, BatchNormalization, Flatten, Input
+from sklearn.model_selection import train_test_split
+
+path = 'data/UTKFace'
+
+images = []
+age = []
+gender = []
+
+for img in os.listdir(path):
+    ages = img.split("_")[0]
+    genders = img.split("_")[1]
+    img = cv2.imread(str(path) + "/" + str(img))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    images.append(np.array(img))
+    age.append(np.array(ages))
+    gender.append(np.array(genders))
+
+images = np.array(images)
+
+images = images / 255.0
+age = np.array(age, dtype=np.int64)
+gender = np.array(gender, dtype=np.int64)
+
+# np.save('preprocessed_data/images.npy', images)
+# np.save('preprocessed_data/age.npy', age)
+# np.save('preprocessed_data/gender.npy', gender)
+
+# images = np.load('preprocessed_data/images.npy')
+# age = np.load('preprocessed_data/age.npy')
+# gender = np.load('preprocessed_data/gender.npy')
+
+X_train_age, X_test_age, y_train_age, y_test_age = train_test_split(images, age, random_state=42)
+X_train_gender, X_test_gender, y_train_gender, y_test_gender = train_test_split(images, gender, random_state=42)
+
+# Define the model - For Age
+age_model = Sequential()
+age_model.add(Conv2D(128, kernel_size=3, activation='relu', input_shape=(200,200,3)))
+age_model.add(MaxPool2D(pool_size=3, strides=2))
+
+age_model.add(Conv2D(128, kernel_size=3, activation='relu'))
+age_model.add(MaxPool2D(pool_size=3, strides=2))
+              
+age_model.add(Conv2D(256, kernel_size=3, activation='relu'))
+age_model.add(MaxPool2D(pool_size=3, strides=2))
+
+age_model.add(Conv2D(512, kernel_size=3, activation='relu'))
+age_model.add(MaxPool2D(pool_size=3, strides=2))
+
+age_model.add(Flatten())
+age_model.add(Dropout(0.2))
+age_model.add(Dense(512, activation='relu'))
+
+age_model.add(Dense(1, activation='linear', name='age'))
+              
+age_model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+
+age_model.summary()
+
+# TODO: Fit for 10 epochs
+with tf.device('/CPU:0'):
+    history_age = age_model.fit(X_train_age, y_train_age,
+                                epochs=10,
+                                validation_data=(X_test_age, y_test_age))  
+
+age_model.save('models/age_model_10epochs.h5')
+
+# Define the model - For Gender
+gender_model = Sequential()
+
+gender_model.add(Conv2D(36, kernel_size=3, activation='relu', input_shape=(200,200,3)))
+
+gender_model.add(MaxPool2D(pool_size=3, strides=2))
+gender_model.add(Conv2D(64, kernel_size=3, activation='relu'))
+gender_model.add(MaxPool2D(pool_size=3, strides=2))
+
+gender_model.add(Conv2D(128, kernel_size=3, activation='relu'))
+gender_model.add(MaxPool2D(pool_size=3, strides=2))
+
+gender_model.add(Conv2D(256, kernel_size=3, activation='relu'))
+gender_model.add(MaxPool2D(pool_size=3, strides=2))
+
+gender_model.add(Conv2D(512, kernel_size=3, activation='relu'))
+gender_model.add(MaxPool2D(pool_size=3, strides=2))
+
+gender_model.add(Flatten())
+gender_model.add(Dropout(0.2))
+gender_model.add(Dense(512, activation='relu'))
+gender_model.add(Dense(1, activation='sigmoid', name='gender'))
+
+gender_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+gender_model.summary()
+
+# TODO: Fit for 10 epochs
+with tf.device('/CPU:0'):
+    history_gender = gender_model.fit(X_train_gender, y_train_gender,
+                                      epochs=10,
+                                      validation_data=(X_test_gender, y_test_gender))
+
+gender_model.save('models/gender_model_10epochs.h5')
+
+# Plot the training and validation accuracy and loss at each epoch - For Age model
+loss = history_age.history['loss']
+val_loss = history_age.history['val_loss']
+epochs = range(1, len(loss) + 1)
+plt.plot(epochs, loss, 'y', label='Training loss')
+plt.plot(epochs, val_loss, 'r', label='Validation loss')
+plt.title('Training and validation loss for Age model')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+acc = history_age.history['accuracy']
+val_acc = history_age.history['val_accuracy']
+plt.plot(epochs, acc, 'y', label='Training Accuracy')
+plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')
+plt.title('Training and validation accuracy for Age model')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.show()
+
+# Plot the training and validation accuracy and loss at each epoch - For Gender model
+loss = history_gender.history['loss']
+val_loss = history_gender.history['val_loss']
+epochs = range(1, len(loss) + 1)
+plt.plot(epochs, loss, 'y', label='Training loss')
+plt.plot(epochs, val_loss, 'r', label='Validation loss')
+plt.title('Training and validation loss for Gender model')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+acc = history_gender.history['accuracy']
+val_acc = history_gender.history['val_accuracy']
+plt.plot(epochs, acc, 'y', label='Training Accuracy')
+plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')
+plt.title('Training and validation accuracy for Gender model')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.show()
+
+
+# Test the model - Gender Model
+my_model_gender = load_model('models/gender_model_3epochs.h5')
+with tf.device('/CPU:0'):
+    predictions = my_model_gender.predict(X_test_gender)
+y_pred = (predictions >= 0.5).astype(int)[:, 0]
+
+from sklearn.metrics import accuracy_score, confusion_matrix
+import seaborn as sns
+
+print(f"Accuracy: {accuracy_score(y_true=y_test_gender, y_pred=y_pred)}")
+
+# Confusion matrix
+cm = confusion_matrix(y_true=y_test_gender, y_pred=y_pred)
+sns.heatmap(cm, annot=True, fmt='d')
+
+# Test the model - Age Model
+
+# Saving the models to huggingface
+from transformers import TFAutoModel
+import tensorflow as tf
+
+# Load your TensorFlow model from the .h5 file
+age_model = tf.keras.models.load_model('models/age_model_3epochs.h5')
+gender_model = tf.keras.models.load_model('models/gender_model_3epochs.h5')
+
+# Save the model weights
+age_model.save_weights('age_model_weights.h5')
+gender_model.save_weights('gender_model_weights.h5')
+
+# Load the architecture of the Hugging Face model you want to use
+# For example, if you're using BERT, you would use TFBertModel
+hf_model_age = TFAutoModel.from_pretrained('bert-base-uncased')
+hf_model_gender = TFAutoModel.from_pretrained('bert-base-uncased')
+
+# Load the weights into the Hugging Face model
+hf_model_age.load_weights('age_model_weights.h5')
+hf_model_gender.load_weights('gender_model_weights.h5')
+
+# Save the Hugging Face model
+hf_model_age.save_pretrained('hf_age_model')
+hf_model_gender.save_pretrained('hf_gender_model')

age_model_3epochs.h5

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+size 274399416

gender_model_3epochs.h5

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+oid sha256:6bb2821e39ea29aaaa4f2ad8d0d06a0cc10282e3de90d81b566012341e558695
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