Files
ruvnet--RuView/v2/crates/cog-person-count/Cargo.toml
T
ruv 2400216920 fix(cog-person-count): flag untrained-class counts low_confidence (ADR-159 A2)
The count head has 8 classes but count_train_results.json only has
support for classes 0/1 (presence, not multi-occupant counting). An
argmax on classes 2..=7 is out-of-distribution, yet the cog emitted it
as a confident headcount and the crate billed itself a "multi-person
counter".

- Add MAX_TRAINED_CLASS=1, CountPrediction::is_low_confidence() and
  clamped_count().
- person.count events now carry low_confidence + raw_count, downgrade to
  level "warn" when OOD, and clamp the reported count to the trained
  range (no fabricated headcount).
- run.started discloses count_max_trained_class / count_classes.
- Cargo.toml description: "multi-person counter" ->
  "presence detector + (data-gated) person count".

Multi-occupant accuracy stays DATA-GATED (not fabricated).

Failing-on-old test: untrained_class_argmax_is_flagged_low_confidence.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-11 23:10:01 -04:00

42 lines
1.3 KiB
TOML

[package]
name = "cog-person-count"
version.workspace = true
edition.workspace = true
authors.workspace = true
license.workspace = true
repository.workspace = true
description = "Cognitum Cog: WiFi-CSI presence detector + (data-gated) person count (ADR-103). Candle-based head trained on classes 0/1 (presence); the 8-class count head ships but counts above the trained range are flagged low_confidence. Stoer-Wagner multi-node fusion."
[[bin]]
name = "cog-person-count"
path = "src/main.rs"
[lib]
name = "cog_person_count"
path = "src/lib.rs"
[dependencies]
clap = { version = "4", features = ["derive"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
thiserror = "1"
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter"] }
tokio = { version = "1", features = ["rt-multi-thread", "macros", "signal", "time"] }
sha2 = "0.10"
ureq = { version = "2", default-features = false, features = ["tls"] }
# Same Candle stack the pose cog uses — CPU by default, `cuda` feature
# opt-in for hosts with a CUDA GPU.
candle-core = { version = "0.9", default-features = false }
candle-nn = { version = "0.9", default-features = false }
safetensors = "0.4"
[dev-dependencies]
tempfile = "3"
approx = "0.5"
[features]
default = []
cuda = ["candle-core/cuda", "candle-nn/cuda"]
hailo = []