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Datasets:
dkalsan
/
FLEDGE

Tasks:
Image Segmentation
Size:
100K<n<1M
ArXiv:
Tags:
semantic-segmentation
synthetic-data
domain-adaptation
cityscapes
acdc
License:
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metadata 
license: gpl-3.0
task_categories:
  - image-segmentation
tags:
  - semantic-segmentation
  - synthetic-data
  - domain-adaptation
  - cityscapes
  - acdc
size_categories:
  - 100K<n<1M

Dataset Summary

This dataset contains generated images for semantic segmentation targeting the Cityscapes and ACDC domains. The images are produced from five source datasets using the method described in: A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation.

Technical Specifications

  • Subset Selection: To preserve storage space, we release the top 3 generated images per original source image, selected based on our proposed MCOC score.
  • Label Mapping: All masks follow the standard Cityscapes 19-class format.
  • Resolution: 512 x 1024 (Height x Width).
  • Format: Images and masks are provided in .png format.

Dataset Statistics

The following table reflects the number of source images used to generate the target domain data.

Source Domain Image Count Status
GTA 24,966 Available
UrbanSyn 7,539 Available
VEIS 3,018 Available
SHIFT 3,000 Available
Synscapes 0 Restricted

Note on Synscapes: Due to licensing restrictions, we are unable to release the generated Synscapes data. The folders contain the original license file for reference but do not include image data.

Dataset Structure

The data is organized by <Target_Domain>/<Source_Domain>/. The three image folders ship as zip archives so the repository stays within Hugging Face's file-count limits — images/ is split into ~2 GB chunks. splits/ and LICENSE are plain files.

<Target_Domain>/
└── <Source_Domain>/
    ├── images_000.zip … images_NNN.zip   # Top 3 generated images per source (MCOC ranked)
    ├── original_tid.zip                  # Original source segmentation maps
    ├── hrda_tid.zip                      # HRDA pseudo-labels
    ├── splits/                           # .txt files for training loading
    └── LICENSE                           # Original source dataset license

Extracting the archives restores the layout the loading scripts expect. Entry names already carry their folder prefix (e.g. images/03666/0005.png), so every archive unpacks into the right place:

<Target_Domain>/
└── <Source_Domain>/
    ├── images/
    ├── original_tid/
    ├── hrda_tid/
    ├── splits/
    └── LICENSE
Pair images chunks Download
cityscapes/gta 22 49.9 GB
cityscapes/urbansyn 7 15.5 GB
cityscapes/shift 3 6.1 GB
cityscapes/veis 3 5.8 GB
acdc/urbansyn 11 23.6 GB
acdc/veis 4 9.0 GB
Total 50 110 GB

Download & Extraction 

pip install -U "huggingface_hub[cli]"

# Everything (~110 GB)
hf download dkalsan/FLEDGE --repo-type dataset --local-dir FLEDGE

# Or a single pair, e.g. ACDC generated from VEIS (~9 GB)
hf download dkalsan/FLEDGE --repo-type dataset --local-dir FLEDGE --include "acdc/veis/*"

Then unpack every archive in place, deleting each one as it succeeds:

cd FLEDGE
for z in */*/*.zip; do
  unzip -q -o "$z" -d "$(dirname "$z")" && rm "$z"
done

Usage Example

Example data loading scripts that utilize the provided .txt split files are available in our official GitHub repository: github.com/vislearn/FLEDGE

Citation 

@misc{kalsan2025fledge,
  title={A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation}, 
  author={Damjan Kalšan and Denis Zavadski and Tim Küchler and Haebom Lee and Stefan Roth and Carsten Rother},
  year={2025},
  eprint={2510.11567},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}