Instructions to use sinuosity/okuafo-maizeguard-edge-v1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use sinuosity/okuafo-maizeguard-edge-v1.4 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Functional smoke-test record
Evidence status: Research-preview maize screening model evaluated on documented development datasets and limited field samples.
These recorded checks used non-redistributed local reference images to verify model loading, tensor shape, preprocessing, and routing behavior. They are implementation evidence only—not an accuracy benchmark, a leakage-safe test set, an agronomist-confirmed diagnosis, or field validation.
| Test | Expected pathway | Recorded status |
|---|---|---|
| Healthy maize development reference | Stage 1 → healthy | Pass — healthy 0.976533 |
| Indoor/non-maize development reference | Stage 1 → not maize/unclear | Pass — not_maize_or_unclear 0.967990 |
| Limited maize field sample; disease label unconfirmed | Stage 1 → Stage 2 | Pass — Stage 1 diseased 0.980578; Stage 2 screened northern_leaf_blight 0.911019 |
Verification was recorded with Python 3.12, TensorFlow 2.20.0, and the packaged FP16 files. The reference images are not included in this staging directory, so the recorded image-level outputs are not independently reproducible from this package alone.
The limited field sample verifies only that the routing and symptom-crop code can run end to end. Its disease label was not independently confirmed by an agronomist, and the predicted class is not accuracy evidence. Field validation is incomplete.
Separately, a retrospective audit of the original v1.4 metadata.csv found
80,313 records, 68,786 source + group IDs, and 2,205 groups crossing
train/validation/test. The historical 0.992/0.993 development metrics are not
leakage-safe release results and must not be presented as public or field
performance. The detailed evidence is in
development-data-audit.json.
corrected-split-plan.json records a deterministic
group-disjoint assignment of 57,914 train, 11,107 validation, and 11,292 test
records, with zero cross-split groups and records. That is a repair plan—not new
training or evaluation evidence. The packaged models have not been retrained or
evaluated on the corrected assignment, and the independent Ghana field holdout
is incomplete.