Datasets:
img_id stringclasses 1
value | label int64 2 2 | z_high_dim unknown | mask unknown | channels int64 256 256 | latent_h int64 32 32 | latent_w int64 32 32 |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
gap-sam-coco-cache
Pre-computed High-Dimensional Forensic Latents for ultra-fast Diffusion-Diff forensics training.
Dataset Details
- Source Dataset:
KhangTruong/COCO-inpainted - Model Extractor:
gap_sam - Cached Channels:
256 - Latent Resolution:
32x32(input256x256) - Format: Parquet with zstd compression
- Status:
completed
Schema / Columns
img_id(string): Image identifier.label(int64): Class label (0: authentic,1: fully synthetic,2: tampered/inpainted).z_high_dim(binary): Pre-computed 256-channel fp16 latent tensor bytes.mask(binary): Ground-truth binary manipulation mask encoded as PNG bytes.channels(int64): Total channel count (256).latent_h(int64): Latent height (32).latent_w(int64): Latent width (32).
High-Speed Training with SID-UNet
sid-train --config configs/experiments/diffusion_diff_minimized/default.yaml \
--cached-hf-repo KhangTruong/gap-sam-coco-cache
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