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title: Forager's Field Station
emoji: 🍄
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 6.16.0
python_version: "3.11"
app_file: app.py
pinned: false
hf_oauth: true
license: apache-2.0
short_description: Pocket-sized intelligence for identifying edible wild foods
---
# Forager's Field Station
Photograph a wild plant or mushroom and the model identifies it — **or refuses
when it isn't sure.** A domain router plus three `tf_efficientnet_lite2`
classifiers (~9M params each), ~0.04B parameters total. The same stack runs
offline on a Hailo 8L NPU in a handheld field device; this Space is its CPU twin.
Built for the **Build Small Hackathon** — Backyard AI track. The honest fit:
a forager in the woods has no signal, so a small on-device model isn't a
compromise, it's the only thing that works.
## How it works
```
photo ─► domain router (berry / mushroom / plant / other)
│ conf < 0.74 or "other" ─► ABSTAIN
▼
ONE expert owns each domain (no cross-expert voting):
berry ─► berry_expert mushroom ─► highvalue_expert
plant ─► medicinals_expert
│ below confidence gate ─► ABSTAIN
▼
SAFE / CAUTION / DEADLY + scientific name, lookalike, key difference
```
Single-expert routing is a safety choice: an off-domain expert never gets to
misclassify an input it doesn't own (e.g. the mushroom expert never sees a
plant, so it can't call a poison hemlock "ramps"). The deadly plants live in the
medicinals expert, which scored 0% toxic-as-edible on held-out validation.
The system is built to **refuse by default.** Across real-world test photos it
abstained rather than guess on the cases it couldn't handle, and never labelled
a deadly specimen as edible.
## Models
| Model | Domain | Classes |
|---|---|---|
| `domain_router_v2` | berry / mushroom / plant / other | 4 |
| `berry_expert` | wild berries + toxic lookalikes | 11 |
| `highvalue_expert` | chanterelles, morels, lion's mane, ginseng… | 11 |
| `medicinals_expert` | wild medicinal plants + toxic lookalikes | 21 |
Trained on iNaturalist research-grade observations. Apache-2.0.
📦 **Weights published as fine-tuned models:** [HomesteaderLabs/forager-field-station-models](https://huggingface.co/HomesteaderLabs/forager-field-station-models) — `.pt` + `.onnx` for the router and all three experts, fine-tuned from `timm/tf_efficientnet_lite2.in1k`.
## Field notes
The build story — edge constraints, the single-expert safety pivot, the
safety-vs-usefulness curve, and a one-line bug that inverted our OOD detector — is in
[FIELD_NOTES.md](FIELD_NOTES.md).
## Safety notice
**Identification aid only — never an authority.** Wild plant and mushroom
identification carries fatal risk. No output should be acted on — including any
consumption decision — without independent verification by a qualified expert.
Amatoxin poisoning (Amanita, Galerina, Conocybe) is lethal with no reliable
field antidote. The maintainers accept no liability for decisions made from
model output.
— [HomesteaderLabs](https://homesteaderlabs.com)
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