In this project a Convolutional Neural Network is used for the feature extraction task.
The implemented model ignore the gap between draws and icons, instead resolves it with a simple Edge Extraction.
https://emizzz.github.io/sketch-to-icon/
I'm also working in order to bridge the gap between the domains, check my python repo: https://github.com/emizzz/Sketch-to-Icon-Paper-Code.
Icons are (the free) part of the beautiful FontAwesome dataset. This project use more than 1500 icons.
The Python CNN:
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(imheight, imwidth, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(680, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()The model was trained on a subset of the famous QuickDraw dataset.
- Mobile friendly