Instructions to use dbaranchuk/check with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbaranchuk/check with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dbaranchuk/check") 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("dbaranchuk/check") model = AutoModelForImageClassification.from_pretrained("dbaranchuk/check") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a239cd27ee72fb01d6954c2fa518b33c71abdb9668e4481ebde974e2aa05141
- Size of remote file:
- 4.67 kB
- SHA256:
- 884c0da23eb49179ddaf339eab7a707a1a6db6a1201d11b3c74a404a5d66ec72
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