Issue
I use following code to train my CNN model with invoice images.
train_datagen = ImageDataGenerator(
rescale = 1. / 255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True
)
test_datagen = ImageDataGenerator(rescale = 1. / 255)
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size)
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size)
model.fit_generator(train_generator,
steps_per_epoch = nb_train_samples // batch_size,
epochs = epochs, validation_data = validation_generator,
validation_steps = nb_validation_samples // batch_size)
The problem is I used only upright images in my training data set. All my images are like following image:
After the training when I want to send an image like below, my model fails to predict its right class.
As you see below, I send horizontal_flip = True to ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale = 1. / 255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True
)
How can I change my code so that it can predict even flipped images. Or should I use manually flipped images within my training dataset?
Solution
I would rotate the images randomly with ImageDataGenerator
. Just specify the following argument:
rotation_range: Int. Degree range for random rotations.
Or, you can pass a preprocessing function to ImageDataGenerator
which gives you more flexibility.
def orthogonal_rot(image):
return np.rot90(image, np.random.choice([-1, 0, 1]))
train_generator = ImageDataGenerator(
preprocessing_function=orthogonal_rot)
This function will rotate by either -90, 0, or 90 degrees.
Answered By - Nicolas Gervais
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