Instructions to use sck/vca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sck/vca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sck/vca") 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("sck/vca") model = AutoModelForImageClassification.from_pretrained("sck/vca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f54f74e9a06b7c4922ea340f0e41395db84f3f6203216004c988ccef74d958e
- Size of remote file:
- 343 MB
- SHA256:
- aac5f2bbc6534142318002f929e8e3237701e9be0f05ca72f1084e1cf897fc73
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