Examples

traffic_light

snake

cloud

power_outlet

tent

eyeglasses

ceiling_fan

paper_clip

beard

spoon

ice_cream

smiley_face

pencil

flower

alarm_clock

bridge

radio

syringe

rifle

knife

t-shirt

face

candle

tooth

bench

bird

ladder

hot_dog

sword

sun

helmet

basketball

donut

hammer

headphones

broom

lollipop

baseball

drums

wheel

cat

spider

clock

grapes

chair

door

moustache

eye

bicycle

coffee_cup

umbrella

anvil

scissors

triangle

bed

light_bulb

laptop

envelope

circle

suitcase

camera

pillow

diving_board

rainbow

pants

bread

saw

line

microphone

screwdriver

key

frying_pan

square

stop_sign

lightning

pizza

cup

cookie

apple

sock

fan

shorts

axe

airplane

butterfly

tennis_racquet

tree

hat

book

shovel

mushroom

dumbbell

cell_phone

wristwatch

baseball_bat

car

mountain

moon

table

star

Architecture of the Neural Network. This figure shows only 2 convolutional layers while the model uses 3 convolutional layers.

The size of the dense layer i.e. n3 = 256 units.

The following figure shows some of the examples correctly identified.

Trained Model:

Thanks to Zaid Alyafeai whose writings inspired this app.

This Convolutional Neural Network (CNN) was trained on the Quick Draw dataset by Google to classify the drawn image into the 100 following classes:The model was the exported into json and binary files and hosted on a server with CORS enabled and is run on client side (your browser) using tensorflow.js. To be able to do so the model was kept simple but was still able to achieve a relative good accuracy. The CNN contains 3 Convolutional - Max pooling pairs of hidden layers folowed by a single Dense hidden layer. The following figure shows the architecture of the Neural Network used.