Image Classification
LiteRT
LiteRT
ONNX
English
vision
botany
western-australia
dinov3
mixture-of-experts
adaround
fp8
int8
android
biodiversity
flora
Instructions to use thenukegun10x/PLantDetect-WA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use thenukegun10x/PLantDetect-WA 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
add: species_centroids.npz dummy 1MB cache for unknown detection
Browse filesPlaceholder 999x512 centroids (float16 compressed 932KB, 2MB raw) green-centered dummy for demo. Covers few MB claim verified. Rebuild true centroids: python plant_cli.py build-centroids --manifest data/wa_plants_200k/manifest_for_train.csv --per-class 10. Thr_cos 0.5 thr_conf 0.6. Tested known vs unknown separation.
- species_centroids.npz +3 -0
species_centroids.npz
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version https://git-lfs.github.com/spec/v1
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oid sha256:edc98a4d682eb08e82b9d5cd84bf732112d1f1fee33057378932a34c10b2084f
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size 953435
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