pollin8-edge — milliwatt-class, species-level pollinator & pest counting on a ULP SoC

Reproducibility artifacts for the paper "Milliwatt-Class Pollinator and Pest Monitoring: A Species-Level Edge AI Pipeline on an Ultra-Low-Power System-on-Chip." A compact, anchor-based YOLOv5p detector (~0.31 M params, INT8) runs wholly on the GreenWaves GAP9 SoC of the SENSEI node and counts nine insect species on-node at sub-100 mW — within roughly a year of battery life on a 3 Ah cell.

Headline results (final data)

• Monotonic accuracy vs. resolution (base mAP@0.5): 192 px = 0.77, 320 px = 0.86, 512 px = 0.91.
• Deployed point = 320 px (a deliberate energy choice; 512 px is the accuracy ceiling but costs ~2× the per-inference energy): centre-matched F₁ 0.82, recall 0.83, per-species count-weighted mean absolute count error 8.5 % (non-cancelling), total count near-unbiased (+4 %).
• Negative result: off-the-shelf class re-balancing hurts counting — image-weighting collapses the abundant honeybee (recall 0.87 → 0.58) onto its rare Batesian-mimic drone fly. This is invisible to standard mAP and only exposed by counting-aligned metrics.
• Measured cost on GAP9: 2.6–13.0 mJ and 51–149 ms across 192–512 px at 40–74 mW.

All numbers above are regenerated from results/metrics/*.csv by the scripts in scripts/ (one-number-one-script: nothing is hand-typed).

Repository layout

pollin8-edge/
├── configs/            network + training-recipe + dataset YAMLs
│   ├── yolov5p_sensei.yaml          the deployed detector (~0.31 M params)
│   ├── hyp_sensei_{base,focal,nwd}.yaml   the four loss-ablation recipes
│   └── insects1201.yaml             dataset / class config
├── src/insect_gap9/    pipeline: prepare/tile data, train, evaluate, confusion,
│                       quantize, simulate_gap9, measure_board, sensei_energy, ...
├── scripts/            analysis (make_figures/tables/values, sig_test, collect_*)
│   └── slurm/          training protocol for the Kuma cluster (see docs/training_kuma.md)
├── results/
│   ├── metrics/        FINAL CSVs: arch sweep, per-species, confidence sweeps,
│   │                   significance (Welch t-test), training curve
│   ├── tables/         LaTeX tables (monitoring, convergence, significance, ablation)
│   └── figures/        paper figures + ALL 9 confusion matrices + training curve
└── docs/               training, deployment, measurement, metrics, rationale, reproducibility

Quick start (analysis only — no GPU needed)

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
# regenerate every paper figure/table/value from the committed metric CSVs:
PYTHONPATH=src python scripts/make_values.py
PYTHONPATH=src python scripts/make_tables.py
PYTHONPATH=src python scripts/make_figures.py
PYTHONPATH=src python scripts/sig_test.py        # significance.csv

Dataset

We use the public Bjerge et al. (2022) benchmark (Zenodo 10.5281/zenodo.7395752): 21,841 labelled training images over nine pollinator/pest species — among them the drone fly, a hoverfly that mimics the honeybee (the source of the mimic-collapse result). The dataset is not redistributed here (respect the upstream licence); fetch and tile it with:

PYTHONPATH=src python -m insect_gap9.download_data     # download the public benchmark
PYTHONPATH=src python -m insect_gap9.tile              # 320 px object-centred train + grid val/test tiles

Camera-trap insects occupy few pixels, so each image is cut into 320 px tiles (object-centred for training; a non-overlapping grid for val/test so counts are never double-scored).

Training → docs/training_kuma.md

Trained from scratch on the Kuma GPU cluster with the classic ultralytics/yolov5 repo (the modern ultralytics package only builds an anchor-free head). The core experiment is a 4 arms × 3 sizes × 3 seeds = 36-task SLURM array sweep (resumable, MIG-parallel). The four arms isolate the loss/imbalance recipe; base (standard BCE, no re-balancing) is the deployed one.

Deployment → docs/gvsoc_deployment.md

The trained network is quantised to 8-bit integer (accelerator requirement) and mapped onto GAP9's NE16 convolution accelerator so weights and the largest activation fit the chip's 1.5 MB of on-chip memory (only the input frame streams from on-package memory at the largest resolution). GVSOC (cycle-accurate) and on-board flows are documented.

Measurement

Per-inference energy and latency are measured on GAP9 silicon for the operator-identical reference detector and read off directly (insect_gap9.sensei_energy); the deployed nine-class network differs only in the final detection convolution (single- → nine-class head, 18 → 42 channels, <2 % of the MACs, matching within 3.5 % at every size). results/metrics/sensei_arch_sweep.csv carries the measured M-MAC / latency / energy / power per resolution. See docs/training_kuma.md and the architecture graph results/figures/arch_graph.pdf.

Supporting evidence

• All nine confusion matrices (results/figures/confusion_*.pdf) — base/focal/NWD/focal-noiw across 192/256/320/512 px — show the deployed baseline keeping the honeybee on the diagonal while image-weighting leaks it onto the drone-fly column.
• Per-arm / per-size metrics (results/metrics/sensei_*.csv) and the build-up ablation (results/tables/ablation.tex).
• Statistical significance of the baseline's mAP lead (results/metrics/significance.csv, results/tables/significance.tex) — Welch's t-test over the three seeds.
• Per-class convergence with resolution (results/figures/convergence.pdf, results/tables/convergence.tex) and the training curve (results/figures/train_curve.pdf, results/metrics/train_curve.csv).

Documentation

Doc	Contents
docs/training_kuma.md	Kuma training protocol (SLURM sweep, tiling, eval, measured energy)
docs/gvsoc_deployment.md	Quantisation + GAP9/GVSOC deployment & on-board flow
docs/reproducibility.md	End-to-end reproduction recipe
docs/rationale.md	Why these design choices (architecture, metrics, loss)
docs/metrics.html	Plain-language guide to the counting-aligned metrics
docs/extending.md	Extending the pipeline (new species, stronger detectors)

Reproducibility notes

• One number, one script, one CSV: every figure/table/value derives from results/metrics/*.csv.
• Determinism: seeds are pinned; the sweep stores three seeds and reports mean ± spread.
• The classic yolov5 repo and the GreenWaves GAP SDK (GAPflow / NNTool / GVSOC) are external; the quantise/simulate scripts degrade gracefully to a documented analytical model when the SDK is absent.

Citation

Please cite the paper. A BibTeX entry will be added on publication.

License

See LICENSE.