Instructions to use sxdave/plant_classification_augment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sxdave/plant_classification_augment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sxdave/plant_classification_augment") 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("sxdave/plant_classification_augment") model = AutoModelForImageClassification.from_pretrained("sxdave/plant_classification_augment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7d20e7d1dd4976ac7af913593484559c91ebb550be06fec2efb2d125434fe1f
- Size of remote file:
- 343 MB
- SHA256:
- 253dc2c8546dfab4a5386fbc0bd908e8b5ae282c35434bfd58de3efa3060f84f
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