# mcordts/cityscapesScripts

README and scripts for the Cityscapes Dataset

Repository: https://github.com/mcordts/cityscapesScripts
Canonical: https://ross.abutalabs.com/products/cityscapesscripts
Language: Python
License: MIT
License Family: permissive
Last push: 2025-09-06T18:32:19+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 40, release rhythm 35, longevity 100
- inputs: {"age_days": 3847, "days_push": 361, "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 2346, forks 608 (observed 2026-08-28T04:06:39.902317+00:00)

## What it is
Official scripts and documentation for the Cityscapes Dataset, a large-scale collection of stereo street-scene videos from 50 cities with pixel-level semantic annotations. The repository provides tools for downloading, preparing, inspecting, and evaluating the dataset for computer vision research.

## Use cases
- download and prepare the cityscapes dataset for semantic segmentation training
- evaluate semantic segmentation predictions against cityscapes ground truth
- convert cityscapes polygon annotations to label images
- work with pedestrian bounding box annotations from citypersons
- inspect cityscapes 3d bounding box vehicle annotations
- set up folder structure for cityscapes stereo video sequences

## When to choose
- you are training or benchmarking semantic segmentation models on urban street scenes
- you need the official evaluation scripts for cityscapes benchmark submissions
- you want to convert or inspect the dataset's json polygon annotations

## When to avoid
- you need a general-purpose image annotation tool rather than cityscapes-specific scripts
- your dataset is not cityscapes and you only want generic segmentation utilities
- you want the dataset images themselves - these are scripts, downloaded separately from the website

## Facets
- artifact type: dataset
- maturity: stable
- function: computer-vision, image-processing, data-science, parser, developer-tools
- domain: computer-vision, machine-learning, autonomous-vehicles, image-processing
- platform: python, cross-platform, cli
- tags: semantic-segmentation, street-scenes, benchmark, annotation-tools, evaluation-scripts, autonomous-driving, datasets

## Member repositories
- mcordts/cityscapesScripts (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:39.902317+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 2026-08-30T02:37:02.194075+00:00, confidence not recorded.
  - readme: https://github.com/mcordts/cityscapesScripts (fetched 2026-08-28T04:06:39.902317+00:00, sha 937c4b948329)
  - registry_pypi: https://pypi.org/pypi/cityscapesscripts/json (fetched 2026-08-29T10:17:34.471597+00:00, sha dc6d090d98ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.