Next is Viji 16.
This was The Runner-Up in 2014, a list VRC challenge.
This was a very big architecture and it had 138 million parameters to be trained.
So it took a lot of time to train.
If you look at the architecture instead of convolutional layers, it does convolutional blocks.
And one block consists of several congressional layers and a max pulling layer at the end to this convolutional
block has to convolutional layers and one max pooling layer.
Next convolutional block has two corners, layers and IMAX Willinger.
And then we are going to have a congressional block.
The last two convolutional blocks have three convolutional lives instead of two congressional leaders.
After processing a two 24 hour to 24 pixel image at the end of all of these convolutional layers, we
had 512 feature maps of seven by seven sites.
These feature maps were fed into a fully connected neural network with so many neurons.
At the end of it, there were 1000 output neurons with softmax activation.
This is because Viji sixteen was run on a problem in which there were 1000 classes into which images
were to be classified.
So it was a huge database.
It had over a million images and those images were to be classified and do 1000 classes.
Classes were of dog breeds.
God breeds a tetra.