Image-level person classification on EUPE-ViT-B features. A 768 pixel image gives 2304 patch tokens at the final layer; layernorm across the 768 channels and max-pool across patches gives one 768-D vector. The decision compares two sums of that vector. The boundary is zero.

python

patches = backbone(image)["x_norm_patchtokens"]      # (2304, 768)
pooled  = layernorm(patches, 768).max(dim=0)         # (768,)
present = pooled[pos_dims].sum() > pooled[neg_dims].sum()

At two dimensions:

person present ⟺ feat[48] > feat[637]

python

from infer import PersonDetector
det = PersonDetector.load('d6')
present = det.predict('image.jpg')

Rules

Dimensions are selected on COCO train2017, 118,287 images, and scored on val2017, 5000 images. The splits are disjoint.

rule	dims	F1	precision	recall	slices	LUT4	CCU2C	ns
`d2`	2	0.8681	0.8685	0.8678	4	7	0	0.40
`d4`	4	0.8817	0.9069	0.8578	10	8	10	0.90
`d6`	6	0.8977	0.9461	0.8541	20	9	20	1.40
`d8`	8	0.9039	0.9475	0.8641	30	9	30	1.90
`d12`	12	0.9068	0.9500	0.8674	60	10	60	2.90
`d16`	16	0.9126	0.9568	0.8723	84	10	84	3.90
`d20`	20	0.9217	0.9587	0.8875	108	11	108	4.90
`d40`	40	0.9307	0.9698	0.8945	266	12	266	9.90

Train and validation F1 differ by 0.0006 at 40 dimensions and by at most 0.0078 across the set. d6 is the default.

Dimensions

d2   48 > 637
d4   48 + 71 > 637 + 90
d6   48 + 71 + 292 > 637 + 90 + 82

Selection is greedy over the 192 dimensions with the largest class-mean separation, alternating sides and adding whichever remaining dimension most improves F1 at a zero boundary. Rules nest; tests/test_rules.py checks the nesting.

Dimension 48 responds to people and to person-associated objects and is suppressed on non-human animals and on non-anthropogenic structures.

Offset

A decision of the form sum(pos) - sum(neg) > t requires t because the two sums carry a relative offset. Sets selected under a zero boundary carry none. A 40-dimension set selected at t = 25.28 scores F1 0.7410 on these images when t is set to zero.

Dimension indices and signs are fixed structure. Each rule has no free parameters and 2 to 40 fixed ones.

Circuit

rtl_gen.py emits one Verilog module per rule. synth.py synthesizes them with nosis for a Lattice ECP5 LFE5U-25F. Counts are LUT4s, carry cells and slices on that device. Inputs are the selected channels as signed INT8, post-layernorm and post-max-pool. Output is one bit, combinational, with no multipliers, no memory and no constants.

d2 contains no adder and is LUT-bound at 0.40 ns. Wider rules are carry-bound, with area and delay linear in dimension count.

tests/test_rtl.py simulates each module against a Python reference under Icarus Verilog, on uniform inputs and on inputs at the decision boundary.

Layout

common/      pooled features, the comparison rule, metrics, named pools
cache.py     pooled feature cache for a COCO split
choose.py    dimension selection on train2017, writes rules.json
verify.py    scoring on val2017, writes eval.json
rtl_gen.py   Verilog generation from rules.json
synth.py     nosis synthesis, writes synth.json
infer.py     loader for every rule
rtl/         one module per rule, all generated
tests/       consistency suite, no backbone or dataset required

Each measured JSON opens with a provenance block naming its generating script and the pool it read. tests/test_artifacts.py enforces the pairing and that selection and scoring name different splits.

Running

pip install -e .
python cache.py --split train2017
python cache.py --split val2017
python choose.py
python verify.py
make synth
make test

COCO_ROOT is the dataset root. BACKBONE is the backbone repo id or a local path. BACKBONE_SRC supplies argus.py from a local directory; otherwise it is fetched from the backbone repo. Caching the two splits is a backbone forward over 123,287 images; every later step reads the cache.

bfloat16 kernels select reduction orders by batch size, so cached values depend on --batch. A cache must be built at one batch size throughout.

Source backbone

EUPE-ViT-B from Meta FAIR (arXiv:2603.22387, Zhu et al., March 2026), distilled from PEcore-G + PElang-G + DINOv3-H+ via a 1.9B proxy teacher. License: FAIR Research License, non-commercial. This classifier is an artifact derived from that backbone's feature geometry.

phanerozoic/zero-parameter-classifier

Zero-Parameter Classifier