# zalandoresearch/fashion-mnist

A MNIST-like fashion product database. Benchmark :point_down:

Repository: https://github.com/zalandoresearch/fashion-mnist
Canonical: https://ross.abutalabs.com/products/fashion-mnist
Homepage: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/
Language: Python
License: MIT
License Family: permissive
Topics: mnist, deep-learning, benchmark, machine-learning, dataset, computer-vision, fashion, fashion-mnist, gan, zalando, convolutional-neural-networks
Last push: 2022-06-13T20:36:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3295, "days_push": 1542, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12810, forks 3071 (observed 2026-08-28T04:10:59.856010+00:00)

## What it is
Fashion-MNIST is a dataset of 70,000 28x28 grayscale Zalando article images across 10 classes, designed as a direct drop-in replacement for the original MNIST dataset. It includes a Python loader and a public benchmark dashboard for comparing machine learning algorithms.

## Use cases
- benchmark image classification models on a harder MNIST alternative
- load a drop-in MNIST replacement for training CNNs
- test machine learning algorithms on fashion product images
- compare model performance on a standard vision dataset
- teach deep learning with a simple but non-trivial dataset
- evaluate GANs on small grayscale images

## When to choose
- you need a standard, well-known image classification benchmark harder than MNIST
- you want a small dataset that loads quickly for prototyping vision models
- you need identical image size and split structure to MNIST for easy swapping

## When to avoid
- you need high-resolution or color images
- you need real-world production-grade fashion image data
- your task requires more than 10 coarse clothing categories

## Facets
- artifact type: dataset
- maturity: stable
- function: machine-learning, deep-learning, computer-vision, data-science, benchmarking
- domain: machine-learning, computer-vision, image-processing, data-science
- platform: python, cross-platform
- tags: fashion-mnist, mnist, image-classification, benchmark-dataset, zalando, grayscale-images

## Member repositories
- zalandoresearch/fashion-mnist (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:59.856010+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:13:46.287597+00:00, confidence not recorded.
  - readme: https://github.com/zalandoresearch/fashion-mnist (fetched 2026-08-28T04:10:59.856010+00:00, sha 9cb4ee5b04fb)
  - homepage: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/ (fetched 2026-08-29T08:09:50.415150+00:00, sha 310184dbc4c3)
- Data as of 2026-08-30T08:39:29.467469+00:00.