stratum-ffhq
Enriched image dataset generated by stratum-hq.
Dataset Summary
- Total images: 70,000
- Version: 0.0.269
- Generated with: stratum-hq v0.1.0
Available Layers
| Layer | Count | Format |
|---|---|---|
| caption | 70,000 | parquet |
| depth | 70,000 | npy_tar |
| dinov3 | 70,000 | npy_tar |
| normal | 70,000 | npy_tar |
| pose | 70,000 | npy_tar |
| seg | 70,000 | npy_tar |
| t5 | 70,000 | npy_tar |
Layer Formats
- caption: Included in the main data parquet (
data/) withimage_id,width,height,aspect_bucket, andcaptioncolumns - dinov3: Tar archives with
dinov3_cls.npy(1024, float16) anddinov3_patches.npy(N×1024, float16) per image - t5: Tar archives with
t5_hidden.npy(512×1024, float16) andt5_mask.npy(512, uint8) per image - pose: Tar archives with
pose.npy(133×3, float16) per image — COCO-WholeBody keypoints in [-1, 1] - seg: Tar archives with
seg.npy(H×W, uint8) per image — 28-class body-part segmentation (Sapiens) - depth: Tar archives with
depth.npy(H×W, float16) per image — relative depth, foreground-masked (Sapiens) - normal: Tar archives with
normal.npy(H×W×3, float16) per image — unit surface normals, foreground-masked (Sapiens)
Reproduction
pip install stratum-hq[all]
stratum process ./your-images/ --output ./dataset/ --passes all --device cuda
stratum publish ./dataset/ --hub-repo timlawrenz/stratum-ffhq --layers caption,dinov3,t5,pose,seg,depth,normal