Instructions to use dima806/rice_type_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/rice_type_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/rice_type_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/rice_type_detection") model = AutoModelForImageClassification.from_pretrained("dima806/rice_type_detection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 316871179a85f36f29096f68df34e30aefc4d6bc7975f2a7e690df9d3ad5931c
- Size of remote file:
- 3.9 kB
- SHA256:
- 13e3552c3940c1b6b4940270a3dec069fec3f011bae5fae8bda43e625497a8ce
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