Instructions to use Bazaar/cv_rock_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_rock_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_rock_classification") 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("Bazaar/cv_rock_classification") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_rock_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a84f8c5b5ec4891000ed39b1b79b395646012d1f87b22d355336a2b7062c5e4
- Size of remote file:
- 343 MB
- SHA256:
- 4eb90c257d36b22b0cdf7a725b61b3943b5c599e0e8583bd58a964b96a65db3c
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