Instructions to use Mo0310/5242_scratch_wbc1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mo0310/5242_scratch_wbc1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mo0310/5242_scratch_wbc1") 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("Mo0310/5242_scratch_wbc1") model = AutoModelForImageClassification.from_pretrained("Mo0310/5242_scratch_wbc1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff16ccefc0de12408435673df9fdab53e3a02f24675a7a214fb3db2168767fd7
- Size of remote file:
- 343 MB
- SHA256:
- 558750da90c573fbb10f85034b07864c2c363717f3f03d10d23d7b1dffc77a9a
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