Link to this sectionCityscapes8データセット#

Link to this sectionはじめに#

The Ultralytics Cityscapes8 dataset is a compact semantic segmentation dataset with 8 images sampled from the Cityscapes dataset: 4 for training and 4 for validation. It is designed for rapid testing, debugging, and experimentation with YOLO semantic segmentation models and training pipelines. Its urban-scene content provides a useful pipeline check before scaling to the full Cityscapes dataset.

Cityscapes8は、フルサイズのCityscapesデータセットと同じ19の評価クラスとlabel_mapping動作を使用しており、YOLO26セマンティックセグメンテーションワークフローと完全に互換性があります。

Link to this sectionデータセット YAML#

Cityscapes8データセットの構成は、データセットパス、クラス名、その他重要なメタデータを指定するデータセットYAMLファイルで定義されています。公式のcityscapes8.yamlファイルは、Ultralytics GitHubリポジトリで確認できます。このYAMLには、小さなパッケージ化されたサブセットのダウンロードURLが含まれています。

ultralytics/cfg/datasets/cityscapes8.yaml

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Cityscapes semantic segmentation dataset (19 classes)
# Documentation: https://docs.ultralytics.com/datasets/semantic/cityscapes8
# Example usage: yolo semantic train data=cityscapes8.yaml model=yolo26n-sem.pt
# parent
# ├── ultralytics
# └── datasets
#     └── cityscapes8 ← downloads here (small subset)
#         └── images
#         └── masks

# Dataset root directory
path: cityscapes8 # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images

masks_dir: masks # semantic mask directory

# Cityscapes 19-class labels
names:
  0: road
  1: sidewalk
  2: building
  3: wall
  4: fence
  5: pole
  6: traffic light
  7: traffic sign
  8: vegetation
  9: terrain
  10: sky
  11: person
  12: rider
  13: car
  14: truck
  15: bus
  16: train
  17: motorcycle
  18: bicycle

# Map source label IDs to train IDs; ignore_label is converted to 255.
label_mapping:
  -1: ignore_label
  0: ignore_label
  1: ignore_label
  2: ignore_label
  3: ignore_label
  4: ignore_label
  5: ignore_label
  6: ignore_label
  7: 0
  8: 1
  9: ignore_label
  10: ignore_label
  11: 2
  12: 3
  13: 4
  14: ignore_label
  15: ignore_label
  16: ignore_label
  17: 5
  18: ignore_label
  19: 6
  20: 7
  21: 8
  22: 9
  23: 10
  24: 11
  25: 12
  26: 13
  27: 14
  28: 15
  29: ignore_label
  30: ignore_label
  31: 16
  32: 17
  33: 18

# Download URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/cityscapes8.zip

Link to this section使用方法#

Cityscapes8データセットで画像サイズ1024、100 エポックの条件でYOLO26n-semモデルをトレーニングするには、以下の例を使用してください。トレーニングオプションの全リストについては、YOLOトレーニングドキュメントを参照してください。

学習例

from ultralytics import YOLO

# Load a pretrained YOLO26n-sem model
model = YOLO("yolo26n-sem.pt")

# Train the model on Cityscapes8
results = model.train(data="cityscapes8.yaml", epochs=100, imgsz=1024)

Link to this section引用と謝辞#

Cityscapesデータセットを研究や開発で使用する場合は、以下の論文を引用してください。

引用

@inproceedings{Cordts2016Cityscapes,
  title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
  author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
  booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016}
}

Special thanks to the Cityscapes team for their ongoing contributions to the autonomous driving and computer vision communities.

Link to this sectionよくある質問 (FAQ)#

Link to this sectionUltralytics Cityscapes8データセットは何に使用されますか？#

The Ultralytics Cityscapes8 dataset is designed for rapid testing and debugging of semantic segmentation models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying YOLO semantic segmentation pipelines, including mask loading, augmentations, validation, and export paths, before scaling to the full Cityscapes dataset. Explore the Cityscapes8 YAML configuration for more details.