Displaying 1 to 6 from 6 results

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_right:

  •    Python

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

fashion - The Fashion-MNIST dataset and machine learning models.

  •    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

dress - :dress: The Hackathon App: Fifty Shades of Dress

  •    Objective-C

This application allows the user to find articles on Zalando API based on specific input color. It was developed in couple of hours in a hackathon and might not be a production ready implementation. It just aims to show how color based search functionality can help the user to find the best match. Color matters (especially to women). Most fashion eCommerce websites only allows to search on basic color groups but if you are looking for a specific color, it is really hard to find it in thousands of listed products.




tailor_made - ✄ Managing a Fashion designer's daily routine.

  •    Dart

TailorMade is what actually started out as an experiment with Flutter, flutter_redux and Firebase Cloud Functions but instead turned out to be a valuable tool for managing a Fashion designer's daily routine. It is clean, easy on the eyes and overall has a very smooth feel. It also handles offline use cases with Firebase Cloud. Logo, Design & Concept by Me. For help getting started with Flutter, view our online documentation.






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