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:
- 3c120bc8709024a945219f99ab70742b3e9899f8b81210de75e8c74795807635
- Size of remote file:
- 113 MB
- SHA256:
- 24579dfab731f4721e6882e1fdf0c000f41aac34c5be4a933b4e4b1be0b092df
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