Can I train a CNN model with Two Image dataset at the same time

I want to train a CNN model with two Image dataset at the same time.

Dataset1(RGB-Images[size(64,64,3)]) ->class-1:Fire Class-2:Non_Fire Dataset2(gray_scale-image[size(120,120,1)])->class-1:Fire Class-2:Non_Fire

Model output:Fire(class) or Non_Fire(class)

My dataset link: Dataset-1:

test_path1='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/gt/test'
train_path1='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/gt/train'
valid_path1='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/gt/valid'

Dataset-2:

test_path2='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/rgb/test'
train_path2='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/rgb/train'
valid_path2='/content/drive/MyDrive/Thesis_Dataset/Devide_Cnn/rgb/valid'

I have found samle code from stackoverflow where used two dataset are Non Image data so that i cant understand how can i apply this code for my own problem .

**Can anyone help me how can i run fit operation on my two datasets using following sample code?????????? *Also can you please someone tell me whether this approach be helpful for accuracy or not??

Sample code from stackoverflow:(show me how can i run fit operarion for my two given image dataset)

from keras.layers import Conv2D, MaxPooling2D, Input, Dense, Flatten, concatenate
from keras.models import Model
import numpy as np

img_input = Input(shape=(64, 64, 1))  ## branch 1 with image input
x = Conv2D(64, (3, 3))(img_input)
x = Conv2D(64, (3, 3))(x)
x = MaxPooling2D((2, 2))(x)
x = Flatten()(x)
out_a = Dense(64)(x)

num_input = Input(shape=(7,))        ## branch 2 with numerical input
x1 = Dense(8, activation='relu')(num_input)
out_b = Dense(16, activation='relu')(x1)

concatenated = concatenate([out_a, out_b])    ## concatenate the two branches
out = Dense(4, activation='softmax')(concatenated)
model = Model([img_input, num_input], out)
print(model.summary())
model.compile('sgd', 'categorical_crossentropy', ['accuracy'])

### Just for sanity check
X = [np.zeros((1,64,64,1)), np.zeros((1,7))]
y = np.ones((1,4))
model.fit(X, y)
print(model.predict(X))