I Built My Own Fashion Dataset For Deep Learning
52,000 labeled fashion images from 10 categories
Link to the dataset (including the scripts used)
When it comes to computer vision, there are two famous image datasets; handwritten digits-MNIST and fashion-MNIST. The latter dataset is provided by Zalando, one of the largest online clothing retailers, and consists of images of ten different clothing categories.
The main issue with the fashion-MNIST dataset is that it is widely acknowledged to be way too easy, overused, and unable to reflect modern computer vision challenges.
With that being the case, I decided to take advantage of this opportunity and build my own fashion dataset similar to fashion-MNIST but slightly more advanced. I wrote a web scraper script that crawled www.zalando.com and extracted the URL of images from 10 different categories as written below.
CATEGORIES = [Jacket, Pants, Jeans, Shorts, T-shirt, Pullover, Bag, Cap, Sandal, Skirt]The URLs of the images were later converted into 28 by 28-pixel images through another script. The conversion process was the most time-consuming part of this project and took 7 days to complete. Here you can see a few examples of the dataset:
