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mcordts/cityscapesScripts resource

README and scripts for the Cityscapes Dataset observed · 2026-08-28

github.com/mcordts/cityscapesScripts · Python · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

50/100

  • Activity 40
  • Release rhythm 35
  • Longevity 100

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 3847
  • days_rel: n/a
  • days_push: 361
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

2346 stars · 608 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

dataset · maturity stable

computer-vision image-processing data-science parser developer-tools computer-vision machine-learning autonomous-vehicles image-processing python cross-platform cli semantic-segmentation street-scenes benchmark annotation-tools evaluation-scripts autonomous-driving datasets

2 sources

Member repositories

RepositoryRoleHealth v2
mcordts/cityscapesScriptsmain50

For agents

markdown · JSON · MCP: product_card(name="mcordts/cityscapesScripts")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem