Random Outfit Generator

I already explained my idea of creating a wardrobe organizer using a CNN.

I then expanded on the idea. Most women wake up and wonder what to wear for the day. Why not use python to generate a random outfit generator using the images uploaded by the user?


Randomly generate an outfit based on the weather. This is to avoid suggesting inappropriate combinations such as gloves in summer.


  1. The app will randomly select articles of clothing and create a complete outfit. It will display :
  • 1 top (shirt,blouse,tshirt etc.)
  • 1 bottom (skirt,jeans,trouser etc.)
  • 1 pair of shoes (heels,boots etc.)

2. In order to achieve this task, I would have to first categorize the clothing into classes based on the weather.

To do this, I require three models:

  • Clothing Category Model: This model will detect what type of clothing is in the image.
  • Fabric Detection Model: This model will detect the fabric used in the garment. This is very important for clothing such as blouses. If a blouse is made of wool it will be more appropriate for winer.
  • Weather Category Model: This model takes the outputs of the previous two models, i.e the type of garment and fabrication of the garment, to decide which weather the garment can be worn in.

3. Finally, I need to randomize the selection that the app makes for each item based on weather data.

Implementation of Deep Learning

Clothing Category Model

I had created a CNN model for the wardrobe organizer app. It was meant to categorize clothing into 10 basic classes. Then, I made the following modifications to increase the ability of the model:

  • Increased the number of neurons in the output layer to 50. The original had only 10. Now that the model can distinguish more categories of clothing, it is easier to tell which ones can be worn for which season. Eg: Shorts in summer.
  • The original model contains 5 layers in total. I added 2 extra hidden layers. This increases the accuracy for the model.

Fabric Detection Model

Now I need to create a CNN that can identify the fabrication of a garment.

Total number of layers: 6

Total number of neurons in the output layer: 1,000

Weather Category Model

Total number of layers: 5

Total number of neurons in output layer: 3

Implementation in Python

The input image goes through the following process:

Clothing category model> Fabric category model>Weather category model> Python randomizer

The Demo App is available on Google Play:


Name: Deep Fashion


@inproceedings{liuLQWTcvpr16DeepFashion, author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2016} }


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