johnmartinsson/bird-species-classification

Baseline Network Model

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johnmartinsson commented

Implement a baseline convolutional neural network model.

"Figure 5 shows a visual representation of our neural network architecture. The network contains 5 convolutional layer, each followed by a max-pooling layer. We insert one dense layer before the final soft-max layer. The dense layer contains 1024 and the soft-max layer 1000 units, generating a probability for each class. We use batch normalization before every convolutional and before the dense layer. The convolutional layers use a rectify activation function. Drop-out is used on the input layer (probability 0.2), on the dense layer (probability 0.4) and on the soft-max layer (probability 0.4). As a cost function we use the single label categorical cross entropy function (in the log domain)."

Architecture

  • Dropout 20%
  • BachNormalization
  • Convolution with 64 5x5 Kernels Stride Size 2x1
  • ReLU Activation
  • MaxPooling with 2x2 Kernels Stride Size 2x2

Four times:

  • BachNormalization
  • Convolution Num. Filters = 64, 128, 256, 256
  • Convolution Kernel Sizes = 5x5, 5x5, 5x5, 3x3
  • Convolution Stride Size = 1x1
  • ReLU Activation
  • MaxPooling with 2x2 Kernels and Stride Size 2x2

Fully Connected

  • Dropout(40%)
  • Dense Layer with 1024 units
  • Dropout(40%)
  • SoftMax Layer with 19 units