Instructions to use HorcruxNo13/swinv2-tiny-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/swinv2-tiny-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/swinv2-tiny-patch4-window8-256") 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("HorcruxNo13/swinv2-tiny-patch4-window8-256") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/swinv2-tiny-patch4-window8-256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 631c951b6c2234fc0fe6c02cda816c18fe5a5301c6240b7cacc529972e4c72a1
- Size of remote file:
- 4.09 kB
- SHA256:
- f402e268f471bca46d29fa2b4aa32f1b7230313c98d517893027f83632586e65
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