Datasetpublic

qbit-glitch/cityscapes-pseudo-labels

Cityscapes Unsupervised Panoptic Pseudo-Labels Pseudo-labels for unsupervised panoptic segmentation on Cityscapes, generated using overclustered k-means semantics + depth-guided instance splitting. Contents Pseudo-Labels Directory Description Files Format pseudo_semantic_raw_k80/ Overclustered k=80 semantic labels ~3.5K PNGs + centroids.npz PNG (values 0-79), train/val split cups_pseudo_labels_depthpro_tau020/ CUPS-format combined… See the full description on the dataset page: https://huggingface.co/datasets/qbit-glitch/cityscapes-pseudo-labels.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Cityscapes Unsupervised Panoptic Pseudo-Labels

Pseudo-labels for unsupervised panoptic segmentation on Cityscapes, generated using overclustered k-means semantics + depth-guided instance splitting.

Pseudo-Labels

Directory	Description	Files	Format
`pseudo_semantic_raw_k80/`	Overclustered k=80 semantic labels	~3.5K PNGs + centroids.npz	PNG (values 0-79), train/val split
`cups_pseudo_labels_depthpro_tau020/`	CUPS-format combined labels (DepthPro tau=0.20)	~9K files	semantic.png + instance.png + .pt per image

Pre-trained Weights

File	Description	Size
`weights/dinov3_vitb16_official.pth`	DINOv3 ViT-B/16 backbone (Meta official format)	327MB
`weights/cups.ckpt`	CUPS Cascade Mask R-CNN checkpoint	916MB

Download

bash

# Everything
huggingface-cli download qbit-glitch/cityscapes-pseudo-labels --repo-type dataset --local-dir ./data/

# Just pseudo-labels (no weights)
huggingface-cli download qbit-glitch/cityscapes-pseudo-labels --repo-type dataset --local-dir ./data/ \
  --include "pseudo_semantic_raw_k80/*" "cups_pseudo_labels_depthpro_tau020/*"

# Just weights
huggingface-cli download qbit-glitch/cityscapes-pseudo-labels --repo-type dataset --local-dir ./data/ \
  --include "weights/*"

# Just DINOv3 backbone
huggingface-cli download qbit-glitch/cityscapes-pseudo-labels --repo-type dataset --local-dir ./data/ \
  --include "weights/dinov3_vitb16_official.pth"

Pipeline

1.Semantic: DINOv2 ViT-B/14 features -> k-means (k=80) overclustering -> 80-class pseudo-semantics
2.Instance: DepthPro monocular depth -> Sobel gradients -> threshold (tau=0.20, A_min=1000) -> instance masks
3.CUPS format: semantic.png (mapped to 27 CAUSE classes) + instance.png + .pt (metadata) per image

Metrics (Cityscapes val, 27-class CAUSE + Hungarian matching)

Component	PQ	PQ_stuff	PQ_things
k=80 semantics + DepthPro instances	28.40	32.08	23.35
CUPS Stage-2 (trained on these labels)	30.26	31.29	28.50

Citation

If you use these pseudo-labels, please cite CUPS (CVPR 2025).