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We saw how deep features were great for image classification.
They allowed us to get quite high accuracy, even with just a little bit of
training data on a particular classification task by first finding
features from this deep learning model that won the 2012 ImageNet competition.
So, that was pretty exciting.
But if you remember the motivation at the beginning,
kind of the sample use case at the beginning of this module,
we're talking about finding images of products that I like.
You know, I like shoes, I'm gonna find shoes that's similar to my shoes.
I want to find images that are similar to in the product set I care about.
So instead of doing an image classification task,
now we're gonna take those deep features and do an image retrieval task.
So let's go ahead and do that, okay?
So we're going to name our notebook, so
I'm gonna talk about Deep Features for Image Retrieval.
And I'm going to hide the header and hide the toolbar,
so we can go ahead and get a little bit more space.
And I'm gonna fire up graphlab create and
I'm also going to load the CIFAR-10
dataset, which is the same dataset that we loaded
earlier when we talked about deep features for classification.
So in this case I'm just going to load the training set because we're
just going to do retrieval in the training set.
So let's start from there.
So I'm gonna call it image_train and
I'm going to use graphlab.Sframe and
then as input I'm going to give it the image_train_data/.
And just as a little reminder, if I look at this image_train data set,
.head, in this s frame, we have the image id,
the actual image, the label which we're not gonna use for
retrieval, we're not gonna use that label at all.
The image array of the pixels, which we're also not gonna use, but
it already has the deep features precomputed.
In the notebook on deep features for classification,
we looked at those lines that you need to compute that column, deep features.
Here, I'm just gonna use those deep features.
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