Image Classification CIFAR-10 – Deep Learning Project in Python with Keras

 

https://data-flair.training/blogs/image-classification-deep-learning-project-python-keras/

Image Classification – Deep Learning Project in Python with Keras

CIFAR-10 is a very popular computer vision dataset. This dataset is well studied in many types of deep learning research for object recognition.

This dataset consists of 60,000 images divided into 10 target classes, with each category containing 6000 images of shape 32*32. This dataset contains images of low resolution (32*32), which allows researchers to try new algorithms. The 10 different classes of this dataset are:

1. Airplane
2. Car
3. Bird
4. Cat
5. Deer
6. Dog
7. Frog
8. Horse
9. Ship
10. Truck

Steps for image classification on CIFAR-10:

1. Load the dataset from keras datasets module

from keras.datasets import cifar10

import matplotlib.pyplot as plt

(train_X,train_Y),(test_X,test_Y)=cifar10.load_data()

2. Plot some images from the dataset to visualize the dataset

n=6

plt.figure(figsize=(20,10))

for i in range(n):

plt.subplot(330+1+i)

plt.imshow(train_X[i])

plt.show()

3. Import the required layers and modules to create our convolution neural net architecture

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import Dropout

from keras.layers import Flatten

from keras.constraints import maxnorm

from keras.optimizers import SGD

from keras.layers.convolutional import Conv2D

from keras.layers.convolutional import MaxPooling2D

from keras.utils import np_utils

4. Convert the pixel values of the dataset to float type and then normalize the dataset

train_x=train_X.astype('float32')

test_X=test_X.astype('float32')

train_X=train_X/255.0

test_X=test_X/255.0

5. Now perform the one-hot encoding for target classes

train_Y=np_utils.to_categorical(train_Y)

test_Y=np_utils.to_categorical(test_Y)

num_classes=test_Y.shape[1]

6. Create the sequential model and add the layers

model=Sequential()

model.add(Conv2D(32,(3,3),input_shape=(32,32,3),

padding='same',activation='relu',

kernel_constraint=maxnorm(3)))

model.add(Dropout(0.2))

model.add(Conv2D(32,(3,3),activation='relu',padding='same',kernel_constraint=maxnorm(3)))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(512,activation='relu',kernel_constraint=maxnorm(3)))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

7. Configure the optimizer and compile the model

sgd=SGD(lr=0.01,momentum=0.9,decay=(0.01/25),nesterov=Fals)

model.compile(loss='categorical_crossentropy',

optimizer=sgd,

metrics=['accuracy'])

8. View the model summary for better understanding of model architecture

model.summary()

9. Train the model

model.fit(train_X,train_Y,

validation_data=(test_X,test_Y),

epochs=10,batch_size=32)

10. Calculate its accuracy on testing data

_,acc=model.evaluate(test_X,test_Y)

print(acc*100)

11. Save the model

model.save("model1_cifar_10epoch.h5")

12. Make a dictionary to map to the output classes and make predictions from the model

results={

0:'aeroplane',

1:'automobile',

2:'bird',

3:'cat',

4:'deer',

5:'dog',

6:'frog',

7:'horse',

8:'ship',

9:'truck'

}

from PIL import Image

import numpy as np

im=Image.open("__image_path__")

# the input image is required to be in the shape of dataset, i.e (32,32,3)

im=im.resize((32,32))

im=np.expand_dims(im,axis=0)

im=np.array(im)

pred=model.predict_classes([im])[0]

print(pred,results[pred])

You can test the result on your custom image input. To improve accuracy, try increasing the epoch count to 25 for training.

Image Classification Project GUI

Here, we will build a graphical user interface for our image classifier. We will build this GUI using Tkinter python library.