(딥러닝) Deep Learning age and gender detection 나이를 추측해보자

https://susanqq.github.io/UTKFace/

UTKFace

import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
from tensorflow.keras.models import Sequential, load_model, Model
from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Dropout, BatchNormalization, Flatten, Input
from sklearn.model_selection import train_test_split

path = "C:/Users/user/Desktop/UTKFace.tar/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))
    

age = np.array(age, dtype = np.int64)
images = np.array(images)
gender = np.array(gender, np.uint64)

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)

## age model

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'])

print(age_model.summary())

history_age = age_model.fit(x_train_age, y_train_age, validation_data=(x_test_age, y_test_age), epochs=50)

age_model.save('age_model_50epochs.h4')

## age model

gneder_model = Sequential()
gneder_model.add(Conv2D(128, kernel_size = 3, activation = 'relu', input_shape=(200,200,3)))

gneder_model.add(MaxPool2D(pool_size=3, strides=2))

gneder_model.add(Conv2D(128, kernel_size = 3, activation = 'relu'))
gneder_model.add(MaxPool2D(pool_size=3, strides=2))

gneder_model.add(Conv2D(256, kernel_size = 3, activation = 'relu'))
gneder_model.add(MaxPool2D(pool_size=3, strides=2))

gneder_model.add(Conv2D(512, kernel_size = 3, activation = 'relu'))
gneder_model.add(MaxPool2D(pool_size=3, strides=2))

gneder_model.add(Flatten())
gneder_model.add(Dropout(0.2))
gneder_model.add(Dense(512, activation  = 'relu'))

gneder_model.add(Dense(1, activation = 'sigmoid', name = 'gender'))
gneder_model.compile(optimizer='adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

print(age_model.summary())

history_age = gneder_model.fit(x_train_gender, y_train_gender, validation_data=(x_test_gender, y_test_gender), epochs=50)

age_model.save('gender_model_50epochs.h4')

history = history_age

loss = history.history['loss']
val_loss = history.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')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

acc = history.history['accuracy']

val_acc = history.history['val_accuracy']


plt.plot(epochs, acc, 'y', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()