Try out Computer Vision

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Inspiration

Major League Hacking is my inspiration.

What it does

It uses computer vision for image processing.

How we built it

I built it using Python.

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from keras.datasets import cifar10

import matplotlib.pyplot as plt

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

n=6

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

for i in range(n):

plt.subplot(330+1+i)

plt.imshow(train_X[i])

plt.show() 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

train_x=train_X.astype('float32')

test_X=test_X.astype('float32')

train_Y=np_utils.to_categorical(train_Y)

test_Y=np_utils.to_categorical(test_Y)

num_classes=test_Y.shape[1]

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'))

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

model.compile(loss='categorical_crossentropy',

optimizer=sgd,

metrics=['accuracy'])

model.fit(train_X,train_Y,

validation_data=(test_X,test_Y),

epochs=10,batch_size=32)

Challenges we ran into

It was a fun challenge.

Accomplishments that we're proud of

I am proud of taking this challenge.

What we learned

I learned Computer Vision.

What's next for Try out Computer Vision

I will explore more about Computer Vision more.

Built With

• computer-vision
• python