Image-to-image person re-identification. Crops in, 512-d unit-norm vectors out;
compare with a dot product (cosine similarity).
Crops are consumed at their natural aspect ratio — no fixed square resize,
which matters for person crops, where aspect varies widely.
Benchmarks
mAP / rank-1 at the model's native 768-d width (see the note on 512-d below).
Best per row in bold.
benchmark
this model
CLIP-ReID ViT-B/16
MarketaJu SigLIP2
Real-camera set (200q / 598g)
0.9230 / 0.9750
0.8169 / 0.8900
0.8692 / 0.9200
Market-1501
0.7360 / 0.8694
0.7784 / 0.9026
0.8090 / 0.9068
DukeMTMC-reID
0.5772 / 0.7527
0.4363 / 0.6346
0.5547 / 0.7365
MSMT17
0.4681 / 0.7117
0.1503 / 0.3831
0.5660 / 0.7899
Cross-domain set (3,000q / 10,415g)
0.3981 / 0.3903
0.3021 / 0.3099
0.4692 / 0.4694
VC-Clothes (clothes-change)
0.4756 / 0.6820
0.4838 / 0.6997
0.6115 / 0.7596
Protocol: gallery entries sharing both identity and camera with the query are
dropped as junk; queries left with no true match are skipped.
Notes:
The real-camera set is an in-house benchmark of fixed-camera surveillance
captures with annotator-verified labels — the closest proxy to deployment
conditions here, and the only row measured on the cameras this model serves.
On it, top-1 errors fall from 22 of 200 (CLIP-ReID) to 9.
The public benchmarks and the real-camera set disagree about who is best, and
that is the point. MarketaJu leads Market-1501, MSMT17 and the cross-domain
set, yet places second on real cameras. Its public wins are consistent with
source overlap: its training corpora (CUHK-PEDES, ICFG-PEDES) are assembled from
Market-1501 and MSMT17 imagery, so those test identities are not necessarily
unseen. DukeMTMC, which appears in none of its sources, is the one public set
where it loses.
Competitor figures were measured through their own published weights and
preprocessing, not reimplementations.
The published 512-d projection retains 99.8% of embedding variance and costs
about half a point on the real-camera set: 0.9180 / 0.9700. The table is
reported at 768-d so the comparison against other models is width-neutral.
Preprocessing: RGB, natural aspect ratio, patch-16 tokenisation to at most 256
patches, mean/std 0.5, BILINEAR. Crops with a short side under 32px or an aspect
ratio beyond 5:1 are scaled to those bounds first — surveillance person crops hit
this guard often, and skipping it changes embeddings measurably.
Mixed patch grids within one batch are handled correctly — the positional resize
happens in-graph per image, so batches need not be aspect-uniform.
