Instructions to use Skullly/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skullly/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Skullly/results") 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("Skullly/results") model = AutoModelForImageClassification.from_pretrained("Skullly/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6042391000363f91833dd085179327c0011b93020a7c8c701e34acc4fc159d3d
- Size of remote file:
- 5.05 kB
- SHA256:
- a5c17f05f881acad21066bc021f10d2d464c4d0d37cb53f0392275bbd9309d4f
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