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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A MNIST-like fashion product database. Benchmark :point_down:
| Date | Stars |
|---|---|
| 2026-07-24 | 12795 |
| 2026-07-25 | 12796 |
| 2026-07-28 | 12796 |
| 2026-07-30 | 12796 |
| 2026-08-06 | 12796 |
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# Fashion-MNIST [](https://github.com/zalandoresearch/fashion-mnist/) [](https://gitter.im/fashion-mnist/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link) [](README.zh-CN.md) [](README.ja.md) [](https://opensource.org/licenses/MIT) [](https://hanxiao.github.io/2018/09/28/Fashion-MNIST-Year-In-Review/) <details><summary>Table of Contents</summary><p> * [Why we made Fashion-MNIST](#why-we-made-fashion-mnist) * [Get the Data](#get-the-data) * [Usage](#usage) * [Benchmark](#benchmark) * [Visualization](#visualization) * [Contributing](#contributing) * [Contact](#contact) * [Citing Fashion-MNIST](#citing-fashion-mnist) * [License](#license) </p></details><p></p> `Fashion-MNIST` is a dataset of [Zalando](https://jobs.zalando.com/tech/)'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](http://yann.lecun.com/exdb/mnist/) for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. Here's an example of how the data looks (*each class takes three-rows*):  <img src="doc/img/embedding.gif" width="100%"> ## Why we made Fashion-MNIST The original [MNIST dataset](http://yann.lecun.com/exdb/mnist/) contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. *"If it doesn't work on MNIST, it **won't work** at all"*, they said. *"Well, if it does work on MNIST, it may still fail on others."* ### To Serious Machine Learning Researchers Seriously, we are talking about replacing MNIST. Here are some good reasons: - **MNIST is too easy.** Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out [our side-by-side benchmark for Fashion-MNIST vs. MNIST](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/), and read "[Most pairs of MNIST digits can be distinguished pretty well by just one pixel](https://gist.github.com/dgrtwo/aaef94ecc6a60cd50322c0054cc04478)." - **MNIST is overused.** In [this April 2017 Twitter thread](https://twitter.com/goodfellow_ian/status/852591106655043584), Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST. - **MNIST can not represent modern CV tasks**, as noted in [this April 2017 Twitter thread](https://twitter.com/fchollet/status/852594987527045120), deep learning expert/Keras author François Chollet. ## Get the Data [Many ML libraries](#loading-data-with-other-machine-learning-libraries) already include Fashion-MNIST data/API, give it a try! You can use direct links to download the dataset. The data is stored in the **same** format as the original [MNIST data](http://yann.lecun.com/exdb/mnist/). | Name | Content | Examples | Size | Link | MD5 Checksum| | --- | --- |--- | --- |--- |--- | | `train-images-idx3-ubyte.gz` | training set images | 60,000|26 MBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz)|`8d4fb7e6c68d591d4c3dfef9ec88bf0d`| | `train-labels-idx1-ubyte.gz` | training set labels |60,000|29 KBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a3e2fd405e19843b, topic:dataset, readme:dataset
matched fp:a3e2fd405e19843b, topic:deep-learning
matched fp:a3e2fd405e19843b, topic:computer-vision
matched fp:a3e2fd405e19843b, topic:gan