Benchmarks
Real measurements where we have them. Schema where we don't. Bad numbers next to good.
Hardware & software
| Board | Raspberry Pi 5 (Model B Rev 1.1), 1 GB |
| OS | Debian Trixie 13.0 · kernel 6.12.75 |
| Python | 3.13.5 |
| Runtime | onnxruntime 1.26.0 (CPU EP) |
| Model | YOLOv8n (stock COCO baseline — not a litter model) · ONNX fp32 · 640×640 input |
| Cooling | bare board, fanless, no active cooling |
| Ambient | 22 °C |
| Run date | 2026-05-13 |
Burst vs sustained
The two regimes are not commensurate — cold-cache peak FPS is a headroom indicator, not what we will run in the field. Cards side-by-side make that explicit.
- p50
- 180 ms
- p95
- 181 ms
- p99
- 182 ms
- regime
- 50 inferences · cold cache
- thermal
- run too short to settle
Reference profile only — not what we will run in the field.
- p50
- 219 ms
- p95
- 226 ms
- p99
- 245 ms
- thermal
- 73.6 °C peak · 0 throttle
- memory
- 140 MiB RSS · flat
- drift
- p50 minute 1 → 60: +0.05 %
- regime
- 7,649 inferences · no error
Low-end, fanless, no active cooling. No GPU.
Sustained-run notes
- 1-hour soak, 7,649 inferences, no error, no thrown frames.
- p50 minute 1 → minute 60: +0.05 % drift. No accumulator pathology observed.
- Thermal: 73.6 °C peak; firmware throttle threshold is 80 °C. 0 throttle events.
- Memory: 140 MiB RSS, flat across the run. No leak.
- Power: USB-C PD 27 W supply (laptop USB-C undervolts and was the bug behind the first run).
These numbers are edge-feasibility evidence: a stock COCO model proving the board can sustain inference. No litter model has been benchmarked on the Pi 5 yet — the litter detector below runs server-side.
Litter detector (server-side)
The detector behind Trash Trail's photo pipeline. v2 (RF-DETR-S, trained 2026-08-02) replaced the v1 YOLO11n baseline after evaluation on a shared internal validation split (826 images / 1,004 boxes):
| Metric | v1 · YOLO11n | v2 · RF-DETR-S |
|---|---|---|
| mAP@0.5, internal val (43 classes) | 0.074 | 0.215 (~3×) |
| Hard-negative false-positive rate | 0.049 | 0.024 (halved) |
| Hazard classes (needles, batteries) recall | 0.0 | 0.0 — unsolved, stated plainly |
| Training cost | — | 8.06 h · one RTX 4070 · 1.613 kWh |
Internal validation only — there is no clean external litter benchmark we trust yet, and the 3× bundles architecture and data improvements together. These are relative signals, not headline accuracy claims.
Not-yet-measured
| Metric | Status | Gating |
|---|---|---|
| Litter-model FPS / accuracy on Pi 5 | — | Edge-sized student model (int8 distillation) |
| Degraded-view recall (buried / submerged) | — | Field photo collection |
| End-to-end latency (capture → plan) | — | Chassis assembly + sensor wiring |
| Thermal envelope at 35 °C ambient | — | Summer enclosure prototype |
| Power draw at sustained inference | — | USB-C PD meter on bench |
The schema is published; first measurements are pending chassis assembly. The schema being visible is the credibility signal even before numbers land.
Human-safety perception
| Adult-detection latency | — |
| Child-detection latency | — |
| Pet-detection latency | — |
| Wheelchair-detection latency | — |
Mechanical safety response
| E-stop latency | — |
| Watchdog cycle | — |
| Brake engagement | — |
| Thermal cutoff @ 80 °C | implemented · never fired (soak peaked 73.6 °C) |
Hazardous-content handling
| Red-list item recall | — |
| Operator escalation latency | — |
| Hazard-pin coverage on routes | — |