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Now, we are going to start with a complete end to end project in this project.

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We will try to classify colored images of cats and dogs.

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So if take up dataset from Google, Google is a website where a lot of data science competitions are

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being held.

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There was a competition which was held in 2003 in which thousands of images of cats and dogs were given

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and a model was to be built to classify those images into cats and dogs.

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The best accuracy achieved in that competition was nearly ninety eight percent.

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We are going to use a subset of that data and try to build our model and really try to achieve over

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90 percent accuracy with our model.

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Here are some of the details of this project.

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This is a binary classification problem, unlike fashion amnesty in which there were 10 categories to

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be predicted.

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Here we have only two.

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Either that images of a cat or it is of a dog.

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So only two glasses.

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That is why it is a binary classification problem.

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Then this is a data set of coloured images.

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That is, we will have three channels are GNB instead of only one channel.

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As we have, in fact, amnesty does it then?

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We do not have a standard dimension of all these images.

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As you saw in the previous project, we were using grindy eight by 28 pixel images.

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But here are dataset does not have one standard dimension.

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So when we are feeding the data to our model, we will have to convert the images to one standard dimension.

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So that is one additional step.

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Then we are using a gaggle dataset.

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If you are interested, you can go to the Kaggle website and see this cat versus dog competition.

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You can also see the leaderboard there.

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How much accuracy people have achieved.

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And you can compare your model with other people's model.

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And the last point is we are going to use a subset of the total data.

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The total data had over 50000 images in our model.

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We are going to use only 4000 images to tell them to train 1000 for validation dataset and 1000 for

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testing.

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So using only this small part of the data, we are still going to achieve accuracy's, which are comparable

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to the other models built by people in the competition.

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So here is how we have structured the data.

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These zip file that you download from the link that we have provided has 4000 images and those images

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are structured in this format.

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So the first folder will have three folders inside of it.

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These three folders will be tighter, green, valid and paste.

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The drain folder will further have two folders.

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These folders will be cats and dogs.

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So class air here is cat and Class B is dogs.

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And this folder, we will have thousand images of cats.

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And in this world that we will have thousand images of dogs.

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Similarly, in validation dataset, there'll be two folders, one containing 500 images of cats, the

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other containing 500 images of dogs in the testing dataset.

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We'll have our own images.

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So in total, there are 4000 images, 2000 will be used for training.

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The Model 1000 will be used for validation set.

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And the last Aldan images will be used for testing the accuracy on previously unseen data.

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So the process we are going to follow while building this project is this first we will be creating

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a CNN model with four convolutional layers.

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So it will have four different conversion layers paired with pooling layers.

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And this model will be able to achieve accuracy in the range of 70 to 75 percent.

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I'm talking about validation, accuracy here.

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So this model will be able to achieve somewhere between 70 to 75.

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Then because we have a small dataset, we can improve the performance of our model by doing data augmentation.

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Data augmentation is the process of creating artificial images using these small dataset that you have.

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So in the second step, we will augment our data.

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And then then our model again, for example, if you have this image of a cat, you can create a new

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image by zooming in a small part of this image, or you can create a new image by rotating this image

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of a cat.

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And there are many more transformations that you can do to this image to create a similar image of a

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cat using an existing image.

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So using one image, you'll be able to create multiple images just by transforming the image a little

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bit.

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Transformations include linear transformations, rotations, zooming in, zooming out, etc..

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So after you do this and you run the model again, you'll be able to achieve an accuracy or 80 percent.

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Lastly, we'll use one of the architectures that we have discussed previously, and we will try to implement

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those learned architectures to try to classify this as a dog dataset, using that Prytania architecture

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will be able to achieve an accuracy over 90 percent.

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So after this project, you'll have understanding of how to import images, how to run binary or multiclass

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classification using CNN and how to use Prytania architectures to solve the problem that you have with

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you.