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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Han Xiao
Han Xiao

This document introduces Fashion-MNIST, a new dataset created by Zalando Research as a drop-in replacement for the MNIST dataset for benchmarking machine learning algorithms. Fashion-MNIST consists of 60,000 training images and 10,000 test images of 10 fashion product categories, formatted similarly to MNIST. It was created to address issues with MNIST being too easy and not representative of modern computer vision tasks. The dataset has gained popularity in the machine learning community as an alternative to MNIST, with over 2,000 stars on GitHub and being supported by several machine learning libraries.

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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Zalando Research Han Xiao Sept 25, 2017 @ Amazon Berlin
About Me Han Xiao Beijinger Senior Research Scientist @ Zalando Research 2.5y engineering experience in Reco and Search teams @ Zalando Ph.D. & M.Sc. in Computer Science @ TU Munich Blog: https://hanxiao.github.io LinkedIn: https://www.linkedin.com/in/hxiao87/
Zalando Research Portfolio Deep Style Insight ● Deep Learning Advanced Image Manipulation ● Generative models ● Deep Learning Natural Language Processing ● NLP ● Recurrent Neural Networks Intelligent Control ● Reinforcement Learning ● Causality ● Bayesian Inference FASHIONDNA VIRTUALWARDROBE SEARCH& CHATBOT (CAUSALATTRIBUTION)
Fashion-MNIST zalandoresearch/fashion-mnist
MNIST vs. Fashion-MNIST MNIST Fashion-MNIST Published in 1997 2017 Content Handwritten digits Fashion assortments Image type 28x28 grayscale #class 10, balanced #training examples 60,000 #test examples 10,000 File format IDX file format, gzipped
What is (not) Fashion-MNIST? ● It is a toy dataset; ● it is a drop-in replacement for MNIST dataset; ● it can be used for benchmarking/testing machine learning algorithms. ● It is not a new challenge to ML community.
Motivation of Fashion-MNIST/Why Move Away from MNIST? MNIST is too easy. MNIST is overused. MNIST can not represent modern CV tasks.
Story behind Fashion-MNIST I was working on some generative models; Validated it on MNIST, found the task is trivial and the digits are boring; Started to grab some images and build my own dataset; Too lazy to write another data loader, so better stored it as the same format as MNIST.
Generative model on MNIST vs. Fashion-MNIST
Building Fashion-MNIST dataset Images are Zalando online assortments' (front-look) photos. Shot by in-house photographers. Class labels are manually annotated by in-house experts.
Processing pipeline
Overview of Fashion-MNIST
Benchmarking classification algorithms
An Aug. 25, the dataset was released on Github
Achievements ● 2K stars on Github in 6 days ● Github trending #2 from 26.08 to 28.08 ● Tons of discussions on Twitter, Reddit, HackerNews, Facebook ● 7+ ML libraries support ● 15+ Benchmarks submitted by researchers all over the world ● 2 translations of README.md
Highlight: Yann LeCun posted this data set
Highlight: 8 machine learning libraries support ● Apache MXNet Gluon (master ver.) ● deeplearn.js ● Kaggle ● Pytorch ● Keras ● Edward ● Tensorflow (master ver.) ● Torch
Highlight: 20+ benchmarks from the world ● 20+ submissions ● Easy to use ● More challenging than MNIST (2 conv layer gives 99.2% vs. 92.5%) ● "Shallow" learning algorithms are under 90% ● Best result is 96.3% given by Wide Residual Networks with Random Erasing Data Augmentation
Highlight: attracted some GANs researcher
Highlight: used in other task Deep Supervised Hashing for Image Retrieval
Highlight: used for education purpose
Happy hacking! QA

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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

  • 1. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Zalando Research Han Xiao Sept 25, 2017 @ Amazon Berlin
  • 2. About Me Han Xiao Beijinger Senior Research Scientist @ Zalando Research 2.5y engineering experience in Reco and Search teams @ Zalando Ph.D. & M.Sc. in Computer Science @ TU Munich Blog: https://hanxiao.github.io LinkedIn: https://www.linkedin.com/in/hxiao87/
  • 3. Zalando Research Portfolio Deep Style Insight ● Deep Learning Advanced Image Manipulation ● Generative models ● Deep Learning Natural Language Processing ● NLP ● Recurrent Neural Networks Intelligent Control ● Reinforcement Learning ● Causality ● Bayesian Inference FASHIONDNA VIRTUALWARDROBE SEARCH& CHATBOT (CAUSALATTRIBUTION)
  • 5. MNIST vs. Fashion-MNIST MNIST Fashion-MNIST Published in 1997 2017 Content Handwritten digits Fashion assortments Image type 28x28 grayscale #class 10, balanced #training examples 60,000 #test examples 10,000 File format IDX file format, gzipped
  • 6. What is (not) Fashion-MNIST? ● It is a toy dataset; ● it is a drop-in replacement for MNIST dataset; ● it can be used for benchmarking/testing machine learning algorithms. ● It is not a new challenge to ML community.
  • 7. Motivation of Fashion-MNIST/Why Move Away from MNIST? MNIST is too easy. MNIST is overused. MNIST can not represent modern CV tasks.
  • 8. Story behind Fashion-MNIST I was working on some generative models; Validated it on MNIST, found the task is trivial and the digits are boring; Started to grab some images and build my own dataset; Too lazy to write another data loader, so better stored it as the same format as MNIST.
  • 9. Generative model on MNIST vs. Fashion-MNIST
  • 10. Building Fashion-MNIST dataset Images are Zalando online assortments' (front-look) photos. Shot by in-house photographers. Class labels are manually annotated by in-house experts.
  • 14. An Aug. 25, the dataset was released on Github
  • 15. Achievements ● 2K stars on Github in 6 days ● Github trending #2 from 26.08 to 28.08 ● Tons of discussions on Twitter, Reddit, HackerNews, Facebook ● 7+ ML libraries support ● 15+ Benchmarks submitted by researchers all over the world ● 2 translations of README.md
  • 16. Highlight: Yann LeCun posted this data set
  • 17. Highlight: 8 machine learning libraries support ● Apache MXNet Gluon (master ver.) ● deeplearn.js ● Kaggle ● Pytorch ● Keras ● Edward ● Tensorflow (master ver.) ● Torch
  • 18. Highlight: 20+ benchmarks from the world ● 20+ submissions ● Easy to use ● More challenging than MNIST (2 conv layer gives 99.2% vs. 92.5%) ● "Shallow" learning algorithms are under 90% ● Best result is 96.3% given by Wide Residual Networks with Random Erasing Data Augmentation
  • 19. Highlight: attracted some GANs researcher
  • 20. Highlight: used in other task Deep Supervised Hashing for Image Retrieval
  • 21. Highlight: used for education purpose