Dhi Labs — honest edge vision AI
Collection
Edge vision AI that refuses to guess: calibrated intervals, refusal gates, provenance, falsification ledgers. Synthetic benchmarks + demo. • 9 items • Updated
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Dhi Labs — Blog
Technical writing from Dhi Labs on honest edge vision AI. Hugging Face has no public API for Posts or Articles, so these are published here as versioned markdown (and linked from the org card and the Dhi Labs collection).
Posts
- AI that refuses to guess — the thesis: calibrated intervals, refusal gates, provenance, and falsification ledgers as product features, each measured rather than asserted.
- Precision-first cross-camera linking (E1) — how a uniqueness guard lifted synthetic site precision from 0.918 to 1.0 (recall traded deliberately), and how weeks-scale memory stays under 2 MB.
- When the error bar is the product (A4) — calibration honesty in amodal-counting: the analytic interval under-covered (0.50–0.63 vs a claimed 0.90), conformal calibration widened it 6.7×–14.5×, and the point-estimate win is modest and openly bounded.
Honesty note
Every number in these posts is measured on synthetic benchmarks with exact ground truth; real-world validation is in progress and marked as such throughout. No state-of-the-art claims, no customer or deployment claims.
- Org: https://huggingface.co/Dhi-Technologies
- Code (private, public release pending): https://github.com/DHI-Technologies-Inc
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