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:
- bea455b2e654de610d2ad80d4ec7fd88b53252ef164add804699e2d2de3e4227
- Size of remote file:
- 4.03 kB
- SHA256:
- dedc864d25f5b57329becf553e8f6928779a4e750d80c6170ac5fe7a2b2d1b7d
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