Codesota · Computer Vision · Image segmentation · CityScapesTasks/Computer Vision/Image segmentation
Image segmentation · benchmark dataset · EN

CityScapes.

Cityscapes is a large-scale dataset for semantic urban scene understanding. It provides high-quality pixel-level (fine) annotations for 5,000 images and coarse annotations for 20,000 images captured across 50 cities. The dataset includes dense semantic segmentation (30 classes), instance segmentation for vehicles and people, stereo pairs, preceding/trailing video frames, and rich metadata (GPS, vehicle odometry). It is used as a benchmark for pixel-level, instance-level, and panoptic semantic labeling.

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§ 06 · Contribute

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Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

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What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies
CityScapes — Image segmentation benchmark · Codesota | CodeSOTA