edgarcancinoe/celebahq_512_id_clusters

celebahq_512 with SRK identity labels

Summary

This dataset is a derived version of jxie/celeba-hq. It keeps the original image set and adds automatically generated identity-group labels derived from face-embedding clustering.

As explained in our experimental setup, we use CelebA-HQ from Karras et al. (2018), specifically the Hugging Face snapshot at revision 7ecc6a45edfb5483ccf2f7df1035d298ffe7c76b. The referenced CelebA-HQ version provides gender labels but no identity annotations. To support identity unlearning, we therefore construct identity labels automatically by clustering the embedding space.

How identity labels were created

We cluster the embedding space using DBSCAN, a density-based method that groups samples according to local similarity without requiring a predefined number of clusters. We use the Scikit-Learn implementation with cosine distance.

Nearest-neighbor cosine-similarity analysis reveals two clear modes, with peaks around s ~= 0.8 and s ~= 0.25. The high-similarity peak corresponds to samples of the same identity, while the lower peak captures ArcFace-similar but distinct individuals. Between these modes, a minimum appears around s ~= 0.4, providing a natural separation threshold.

For this release, we use:

• cosine distance threshold eps = 0.35
• minimum samples per cluster min_samples = 2

This procedure produces 9,683 clusters over 27,996 images, which we use as identity labels during training. Manual inspection confirms that the resulting clusters are visually consistent. Empirically, similarities above s > 0.6 almost always correspond to the same individual, with only rare exceptions arising from lighting changes or strong facial occlusions.

Columns

• image: image file
• file_name: original file name
• cluster_id: DBSCAN-derived identity label
• cluster_size: number of images assigned to that identity cluster

Notes

• cluster_id values are automatically generated labels, not official CelebA-HQ person identifiers.
• The identity annotations are derived from embedding clustering rather than provided by the source dataset.
• By default, this export keeps only the image and cluster-related columns needed for downstream identity-unlearning experiments.

Source references

• Original dataset snapshot: jxie/celeba-hq @ 7ecc6a45edfb5483ccf2f7df1035d298ffe7c76b
• Original paper: Karras et al., 2018, "A Style-Based Generator Architecture for Generative Adversarial Networks"