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Overlap catalog: Egocentric-100K
Perceptual fingerprints of builddotai/Egocentric-100K, computed with overlap at 4 fps on decoded pixels - so re-encoding, container swaps and metadata stripping do not hide a match.
Import this into a local overlap index and every dataset you are offered is screened against the public corpus:
overlap import block00 block01 ... # or just the blocks you want
overlap compare vendor-offer.ovlm # "81% of this is public Egocentric-100K"
What is here
- source revision:
fae604b751b25337d6fd8c4c53e595910c28f68f - all 29,966 archives hashed (2048 tasks, 25.4 TB of source video)
- 2,002,872 clips, 100,011 hours, ~1.3 billion fingerprints at 4 fps
- 16 blocks, each a complete, independently importable manifest with its own Merkle root - fetch only the ones you need
archive-index.jsonmaps every source archive to the block and part holding itprovenance.jsonlmaps clip to archive to sha256
Exact duplicates found in the source dataset
Fingerprinting the whole corpus surfaced something worth stating: Egocentric-100K
contains 12,672 byte-identical duplicate clips (0.63% of the corpus),
some appearing three times, and 44 archives that are exact copies
of another archive. Those are stored once here, under the first path seen; the
duplicate archive names are listed in archive-index.json under
duplicate_archives with the archive they duplicate.
Honest limits
- No crop ladder (
crop_hashesis null). Cropped or zoomed re-encodes of this footage will not be found by an index built only from this manifest - a manifest carries no pixels, so crop geometries cannot be derived from it. - Exported at full density (
--stride 1, 4 fps). A manifest strided below that loses recall badly against re-cut footage. algo_id=pdq2,prep_id=p1. A comparison against a different recipe refuses to run rather than returning a wrong answer.
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