On Demand Fashion Apparel 1000 Product Limit
Mode-MNIST
Table of Contents
- Why we made Fashion-MNIST
- Get the Data
- Usage
- Benchmark
- Visualization
- Contributing
- Contact
- Citing Fashion-MNIST
- License
Fashion-MNIST is a dataset of Zalando'south commodity 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 directly drib-in replacement for the original MNIST dataset for benchmarking auto learning algorithms. It shares the same image size and structure of preparation and testing splits.
Here'due south an case of how the data looks (each class takes three-rows):
Why we made Fashion-MNIST
The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it every bit a benchmark to validate their algorithms. In fact, MNIST is often the showtime dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does piece of work on MNIST, it may still fail on others."
To Serious Machine Learning Researchers
Seriously, we are talking nigh replacing MNIST. Here are some good reasons:
- MNIST is as well piece of cake. Convolutional nets tin can accomplish 99.7% on MNIST. Archetype auto learning algorithms can also reach 97% hands. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just ane pixel."
- MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to motility away from MNIST.
- MNIST can non stand for modern CV tasks, as noted in this April 2017 Twitter thread, deep learning practiced/Keras author François Chollet.
Become the Data
Many ML libraries already include Fashion-MNIST data/API, give information technology a try!
Yous tin can use direct links to download the dataset. The data is stored in the same format as the original MNIST data.
| Name | Content | Examples | Size | Link | MD5 Checksum |
|---|---|---|---|---|---|
train-images-idx3-ubyte.gz | preparation set images | 60,000 | 26 MBytes | Download | 8d4fb7e6c68d591d4c3dfef9ec88bf0d |
train-labels-idx1-ubyte.gz | preparation set labels | 60,000 | 29 KBytes | Download | 25c81989df183df01b3e8a0aad5dffbe |
t10k-images-idx3-ubyte.gz | test gear up images | 10,000 | 4.iii MBytes | Download | bef4ecab320f06d8554ea6380940ec79 |
t10k-labels-idx1-ubyte.gz | test set labels | 10,000 | 5.1 KBytes | Download | bb300cfdad3c16e7a12a480ee83cd310 |
Alternatively, y'all can clone this GitHub repository; the dataset appears nether data/manner. This repo besides contains some scripts for criterion and visualization.
git clone git@github.com:zalandoresearch/fashion-mnist.git Labels
Each training and test instance is assigned to ane of the following labels:
| Label | Description |
|---|---|
| 0 | T-shirt/acme |
| ane | Trouser |
| 2 | Pullover |
| iii | Dress |
| 4 | Coat |
| 5 | Sandal |
| 6 | Shirt |
| vii | Sneaker |
| 8 | Bag |
| nine | Ankle kicking |
Usage
Loading data with Python (requires NumPy)
Use utils/mnist_reader in this repo:
import mnist_reader X_train, y_train = mnist_reader.load_mnist('data/mode', kind = 'train') X_test, y_test = mnist_reader.load_mnist('data/way', kind = 't10k') Loading data with Tensorflow
Brand sure you take downloaded the data and placed it in information/mode. Otherwise, Tensorflow will download and utilise the original MNIST.
from tensorflow.examples.tutorials.mnist import input_data data = input_data.read_data_sets('data/fashion') information.railroad train.next_batch(BATCH_SIZE) Note, Tensorflow supports passing in a source url to the read_data_sets. You may use:
data = input_data.read_data_sets('data/mode', source_url = 'http://fashion-mnist.s3-website.eu-key-1.amazonaws.com/') Also, an official Tensorflow tutorial of using tf.keras, a high-level API to railroad train Way-MNIST tin be institute hither.
Loading information with other auto learning libraries
To engagement, the post-obit libraries have included Fashion-MNIST as a congenital-in dataset. Therefore, yous don't need to download Mode-MNIST by yourself. Only follow their API and you are prepare to go.
- Activeloop Hub
- Apache MXNet Gluon
- TensorFlow.js
- Kaggle
- Pytorch
- Keras
- Edward
- Tensorflow
- TensorFlow Datasets
- Torch
- JuliaML
- Chainer
- HuggingFace Datasets
You are welcome to make pull requests to other open-source machine learning packages, improving their back up to Fashion-MNIST dataset.
Loading data with other languages
Equally i of the Auto Learning community's most pop datasets, MNIST has inspired people to implement loaders in many different languages. You lot can utilise these loaders with the Mode-MNIST dataset also. (Note: may require decompressing outset.) To date, we haven't all the same tested all of these loaders with Fashion-MNIST.
- C
- C++
- Java
- Python and this
- Scala
- Become
- C#
- NodeJS and this
- Swift
- R and this
- Matlab
- Carmine
- Rust
Benchmark
We built an automatic benchmarking system based on scikit-acquire that covers 129 classifiers (but no deep learning) with unlike parameters. Find the results here.
You can reproduce the results by running benchmark/runner.py. Nosotros recommend edifice and deploying this Dockerfile.
Y'all are welcome to submit your benchmark; simply create a new issue and we'll list your results here. Before doing that, please make sure it does not already appear in this listing. Visit our correspondent guidelines for additional details.
The table below collects the submitted benchmarks. Notation that we haven't nonetheless tested these results. You lot are welcome to validate the results using the lawmaking provided past the submitter. Test accuracy may differ due to the number of epoch, batch size, etc. To right this table, please create a new issue.
| Classifier | Preprocessing | Fashion test accuracy | MNIST examination accuracy | Submitter | Lawmaking |
|---|---|---|---|---|---|
| ii Conv+pooling | None | 0.876 | - | Kashif Rasul | |
| 2 Conv+pooling | None | 0.916 | - | Tensorflow'southward md | |
| ii Conv+pooling+ELU activation (PyTorch) | None | 0.903 | - | @AbhirajHinge | |
| 2 Conv | Normalization, random horizontal flip, random vertical flip, random translation, random rotation. | 0.919 | 0.971 | Kyriakos Efthymiadis | |
| 2 Conv <100K parameters | None | 0.925 | 0.992 | @hardmaru | |
| 2 Conv ~113K parameters | Normalization | 0.922 | 0.993 | Abel Grand. | |
| 2 Conv+3 FC ~1.8M parameters | Normalization | 0.932 | 0.994 | @Xfan1025 | |
| 2 Conv+3 FC ~500K parameters | Augmentation, batch normalization | 0.934 | 0.994 | @cmasch | |
| two Conv+pooling+BN | None | 0.934 | - | @khanguyen1207 | |
| 2 Conv+2 FC | Random Horizontal Flips | 0.939 | - | @ashmeet13 | |
| three Conv+ii FC | None | 0.907 | - | @Cenk BircanoΔlu | |
| 3 Conv+pooling+BN | None | 0.903 | 0.994 | @meghanabhange | |
| iii Conv+pooling+2 FC+dropout | None | 0.926 | - | @Umberto Griffo | |
| iii Conv+BN+pooling | None | 0.921 | 0.992 | @gchhablani | |
| 5 Conv+BN+pooling | None | 0.931 | - | @Noumanmufc1 | |
| CNN with optional shortcuts, dumbo-like connectivity | standardization+augmentation+random erasing | 0.947 | - | @kennivich | |
| GRU+SVM | None | 0.888 | 0.965 | @AFAgarap | |
| GRU+SVM with dropout | None | 0.897 | 0.988 | @AFAgarap | |
| WRN40-iv eight.9M params | standard preprocessing (mean/std subtraction/sectionalisation) and augmentation (random crops/horizontal flips) | 0.967 | - | @ajbrock | |
| DenseNet-BC 768K params | standard preprocessing (mean/std subtraction/segmentation) and augmentation (random crops/horizontal flips) | 0.954 | - | @ajbrock | |
| MobileNet | augmentation (horizontal flips) | 0.950 | - | @θεζ | |
| ResNet18 | Normalization, random horizontal flip, random vertical flip, random translation, random rotation. | 0.949 | 0.979 | Kyriakos Efthymiadis | |
| GoogleNet with cantankerous-entropy loss | None | 0.937 | - | @Cenk BircanoΔlu | |
| AlexNet with Triplet loss | None | 0.899 | - | @Cenk BircanoΔlu | |
| SqueezeNet with cyclical learning rate 200 epochs | None | 0.900 | - | @snakers4 | |
| Dual path network with wide resnet 28-10 | standard preprocessing (hateful/std subtraction/segmentation) and augmentation (random crops/horizontal flips) | 0.957 | - | @Queequeg | |
| MLP 256-128-100 | None | 0.8833 | - | @heitorrapela | |
| VGG16 26M parameters | None | 0.935 | - | @QuantumLiu | |
| WRN-28-x | standard preprocessing (mean/std subtraction/division) and augmentation (random crops/horizontal flips) | 0.959 | - | @zhunzhong07 | |
| WRN-28-x + Random Erasing | standard preprocessing (mean/std subtraction/segmentation) and augmentation (random crops/horizontal flips) | 0.963 | - | @zhunzhong07 | |
| Human Performance | Crowd-sourced evaluation of human (with no fashion expertise) performance. 1000 randomly sampled test images, 3 labels per paradigm, majority labelling. | 0.835 | - | Leo | - |
| Capsule Network 8M parameters | Normalization and shift at most 2 pixel and horizontal flip | 0.936 | - | @XifengGuo | |
| Sus scrofa+SVM | Hog | 0.926 | - | @subalde | |
| XgBoost | scaling the pixel values to mean=0.0 and var=1.0 | 0.898 | 0.958 | @anktplwl91 | |
| DENSER | - | 0.953 | 0.997 | @fillassuncao | |
| Dyra-Internet | Rescale to unit interval | 0.906 | - | @Dirk SchΓ€fer | |
| Google AutoML | 24 compute hours (higher quality) | 0.939 | - | @Sebastian Heinz | |
| Fastai | Resnet50+Fine-tuning+Softmax on last layer'south activations | 0.9312 | - | @Sayak | |
Other Explorations of Fashion-MNIST
Fashion-MNIST: Twelvemonth in Review
Fashion-MNIST on Google Scholar
Generative adversarial networks (GANs)
- Tensorflow implementation of various GANs and VAEs. (Recommend to read! Note how various GANs generate different results on Way-MNIST, which can non be easily observed on the original MNIST.)
- Make a ghost wardrobe using DCGAN
- mode-mnistηganη©ε ·
- CGAN output after 5000 steps
- GAN Playground - Explore Generative Adversarial Nets in your Browser
Clustering
- Xifeng Guo's implementation of Unsupervised Deep Embedding for Clustering Analysis (DEC)
- Leland McInnes'south Uniform Manifold Approximation and Projection (UMAP)
Video Tutorial
Motorcar Learning Meets Mode past Yufeng G @ Google Deject
Introduction to Kaggle Kernels by Yufeng G @ Google Cloud
ε¨ζε¦ζ·±εΊ¦ε¦δΉ by Mu Li @ Amazon AI
Apache MXNetμΌλ‘ λ°°μ보λ λ₯λ¬λ(Deep Learning) - κΉλ¬΄ν (AWS μ루μ μ¦μν€ν νΈ)
Visualization
t-SNE on Fashion-MNIST (left) and original MNIST (right)
PCA on Fashion-MNIST (left) and original MNIST (correct)
UMAP on Fashion-MNIST (left) and original MNIST (right)
PyMDE on Fashion-MNIST (left) and original MNIST (right)
Contributing
Thanks for your interest in contributing! There are many means to get involved; showtime with our contributor guidelines and then check these open up issues for specific tasks.
Contact
To discuss the dataset, please use
Citing Style-MNIST
If y'all use Manner-MNIST in a scientific publication, we would appreciate references to the following newspaper:
Fashion-MNIST: a Novel Image Dataset for Benchmarking Auto Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747
Biblatex entry:
@online{xiao2017/online, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, title = {Style-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, date = {2017-08-28}, year = {2017}, eprintclass = {cs.LG}, eprinttype = {arXiv}, eprint = {cs.LG/1708.07747}, } Who is citing Fashion-MNIST?
License
The MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com
Permission is hereby granted, free of accuse, to any person obtaining a re-create of this software and associated documentation files (the "Software"), to deal in the Software without brake, including without limitation the rights to employ, re-create, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright discover and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF Whatever KIND, EXPRESS OR Implied, INCLUDING BUT Non Express TO THE WARRANTIES OF MERCHANTABILITY, Fitness FOR A Detail PURPOSE AND NONINFRINGEMENT. IN NO Consequence SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY Merits, Amercement OR OTHER LIABILITY, WHETHER IN AN Activity OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN Connexion WITH THE SOFTWARE OR THE Utilise OR OTHER DEALINGS IN THE SOFTWARE.
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