Instructions to use acrowth/preesme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acrowth/preesme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="acrowth/preesme") 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("acrowth/preesme") model = AutoModelForImageClassification.from_pretrained("acrowth/preesme") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d5895dc2f0c77108735d301099acb69f73503623d33302ace0e6e05e07166d9
- Size of remote file:
- 343 MB
- SHA256:
- 5a8f933afc7be9ee46bbfca2e065d0cec905940fbfd50763165d03eb6fbabb42
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