Image Classification
ultralytics
LiteRT
Bengali
English
yolo26-cls
agriculture
bangladesh
crop-disease
yolo26
on-device
rice
chashibhai
Instructions to use Shaq2/chashibhai-rice-disease-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Shaq2/chashibhai-rice-disease-cls with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Shaq2/chashibhai-rice-disease-cls") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Document independent machine validation for rice TFLite
Browse files
README.md
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@@ -72,8 +72,13 @@ Do **not** letterbox. Letterbox disagreed with the `.pt` on non-square photos.
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| Preprocess verified vs `.pt` | yes (variant C, 100% top-1 vs `.pt` on the rice hold-out used for export) |
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| FP16 vs FP32 top-1 agreement | 1.0 |
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| FP16 vs FP32 max softmax diff | 0.000372171 |
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| Bundled in APK | yes (rice) |
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## Intended use
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- On-device diagnosis in ChashiBhAI for Bangladeshi farmers (Bangla-first UI).
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| Preprocess verified vs `.pt` | yes (variant C, 100% top-1 vs `.pt` on the rice hold-out used for export) |
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| FP16 vs FP32 top-1 agreement | 1.0 |
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| FP16 vs FP32 max softmax diff | 0.000372171 |
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| Independent machine test | pass (August 2026) |
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| Bundled in APK | yes (rice) |
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## Validation
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Independent test on a **separate machine** (August 2026) confirmed this TFLite export loads and classifies correctly with the variant-C contract in `labels.json`. Rice, brassica, and corn all passed the same check.
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## Intended use
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- On-device diagnosis in ChashiBhAI for Bangladeshi farmers (Bangla-first UI).
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