At the age of 17, I was introduced to Polyvore. A cool website which allowed user’s to put together outfits in any way they liked. I loved it! It gave me the idea to create an app which worked in a similar way. This idea blossomed in my mind when AI was not yet as advanced as it is now. Later on in this post, I will cover how I have updated the concept using Deep Learning.
Enable women to experiment with their clothing virtually and come up with new combinations, rather than wasting hours actually changing into different clothing.
- The user takes images of their clothing. It does not have to be artistic, it could even be a shot of just one detail of the garment (for example a beaded collar). The objective is for the user to be able to recognise the garment later on.
- The user then selects the type of clothing they just took a picture of from the list displayed on the screen.
- The user can then retrieve the images later.
I was unhappy with the method above because it is not user friendly. There is too much effort that the user has to put in. I needed to simplify the process. Cue, the magic of Deep Learning.
Applying Deep Learning
Now, I came across a dataset known as the “Fashion MNIST“. This is the solution to my problem! By designing and training a model using the Fashion MNIST dataset, I can now have the app do the sorting for the user. All the user has to do is take images of garments in their wardrobe.
For more information on Fashion MNIST, click the button below:
Implementation in Tensorflow 2.0 with Keras
ANN Used: Convolutional Neural Network
Target Accuracy: 90% to 95%
Accuracy Achieved: 89.5%
The demo app is available on Google Play: