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Next is Viji 16.

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This was The Runner-Up in 2014, a list VRC challenge.

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This was a very big architecture and it had 138 million parameters to be trained.

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So it took a lot of time to train.

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If you look at the architecture instead of convolutional layers, it does convolutional blocks.

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And one block consists of several congressional layers and a max pulling layer at the end to this convolutional

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block has to convolutional layers and one max pooling layer.

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Next convolutional block has two corners, layers and IMAX Willinger.

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And then we are going to have a congressional block.

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The last two convolutional blocks have three convolutional lives instead of two congressional leaders.

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After processing a two 24 hour to 24 pixel image at the end of all of these convolutional layers, we

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had 512 feature maps of seven by seven sites.

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These feature maps were fed into a fully connected neural network with so many neurons.

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At the end of it, there were 1000 output neurons with softmax activation.

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This is because Viji sixteen was run on a problem in which there were 1000 classes into which images

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were to be classified.

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So it was a huge database.

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It had over a million images and those images were to be classified and do 1000 classes.

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Classes were of dog breeds.

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God breeds a tetra.