Suppose you are using YOLO on a 19×19 grid, on a detection problem with 20 classes, and with 5 anchor boxes. During training, for each image you will need to construct an output volume y as the target value for the neural network; this corresponds to the last layer of the neural network. What is the dimension of this output volume?            a.19x19x(5×20)b.19x19x(5×25)c.19x19x(25×20)d.19x19x(20×25)

In YOLO grid size is 17x17, the number of anchor boxes are 5 and able to detect 80 classes .What will be the size of the output layer.*1 point2427632946476852250

You have an input volume that is 15*15*8. You convolve it with 10 filters of size 3*3 using stride=2 and pad=2. What is the dimension of the resulting volume?a.9*9*10b.9*9*12c.9*9*8d.9*9*3

In YOLO 2, the training is done on different image sizes starting from ______ to ________*1 point32x32 , 512x51264x64 , 1024x 102432x320 , 608x680320x320 ,608x608

Question 2How many dimensions is the input for the following neural network object:45678910111213321        self.linear1=nn.Linear(D_in,H)        self.linear2=nn.Linear(H,D_out)             def forward(self,x):        x=torch.sigmoid(self.linear1(x))          x=torch.sigmoid(self.linear2(x))        return x        model=Net(4,10,1)        super(Net,self).__init__()    def __init__(self,D_in,H,D_out):class Net(nn.Module):

Suppose we have a convolutional neural network. The first layer is a convolutional layer, and it has an output volume with dimensions 8x8x8. Suppose the second layer is a fully connected layer with 5 neurons. How many weights does the second layer have?