Figure 1.3 Architecture of ZFNet.
ZFNet uses cross-entropy loss error function, ReLU activation function, and batch stochastic gradient descent. Training is done on 1.3 million images uses a GTX 580 GPU and it takes 12 days. The ZFNet architecture consists of five convolutional layers, followed by three max-pooling layers, and then by three fully connected layers, and a softmax layer as shown in Figure 1.3. Table 1.4 shows an input image 224 × 224 × 3 and it is processing at each layer and shows the filter size, window size, stride, and padding values across each layer. ImageNet top-5 error improved from 16.4% to 11.7%.
1.2.4 VGGNet
Simonyan and Zisserman et al. [4] introduced VGGNet for the ImageNet Challenge in 2014. VGGNet-16 consists of 16 layers; accepts a 227 × 227 × 3 RGB image as input, by subtracting global mean from each pixel. Then, the image is fed to a series of convolutional layers (13 layers) which uses a small receptive field of 3 × 3 and uses same padding and stride is 1. Besides, AlexNet and ZFNet uses max-pooling layer after convolutional layer. VGGNet does not have max-pooling layer between two convolutional layers with 3 × 3 filters and the use of three of these layers is more effective than a receptive field of 5 × 5 and as spatial size decreases, the depth increases. The max-pooling layer uses a window of size 2 × 2 pixel and a stride of 2. It is followed by three fully connected layers; first two with 4,096 neurons and third is the output layer with 1,000 neurons, since ILSVRC classification contains 1,000 channels. Final layer is a softmax layer. The training is carried out on 4 Nvidia Titan Black GPUs for 2–3 weeks with ReLU nonlinearity activation function. The number of parameters is decreased and it is 138 million parameters (522 MB). The test set top-5 error rate during competition is 7.1%. Figure 1.4 shows