Object Detection


By Prof. Seungchul Lee
http://iai.postech.ac.kr/
Industrial AI Lab at POSTECH

Table of Contents

1. 2D Convolution

tf.keras.layers.Conv2D(filters, kernel_size, strides, padding, activation, kernel_regularizer, input_shape)
    filters = 32 
    kernel_size = (3,3)
    strides = (1,1)
    padding = 'SAME'
    activeation='relu'
    kernel_regularizer=tf.keras.regularizers.l2(0.04)
    input_shape = tensor of shape([input_h, input_w, input_ch])


  • filter size
    • the number of channels.
  • kernel_size

    • the height and width of the 2D convolution window.
  • stride

    • the step size of the kernel when traversing the image.
  • padding

    • how the border of a sample is handled.
    • A padded convolution will keep the spatial output dimensions equal to the input, whereas unpadded convolutions will crop away some of the borders if the kernel is larger than 1.
    • 'SAME' : enable zero padding
    • 'VALID' : disable zero padding
  • activation
    • Activation function to use.
  • kernel_regularizer

    • Initializer for the kernel weights matrix.
  • input and output channels

    • A convolutional layer takes a certain number of input channels ($C$) and calculates a specific number of output channels ($D$).

Examples


input = [None, 4, 4, 1]
filter size = [3, 3, 1, 1]
strides = [1, 1, 1, 1]
padding = 'VALID'

input = [None, 5, 5, 1]
filter size = [3, 3, 1, 1]
strides = [1, 1, 1, 1]
padding = 'SAME'

2. Object Detection