Instructions to use microsoft/swinv2-tiny-patch4-window16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-tiny-patch4-window16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-tiny-patch4-window16-256") 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("microsoft/swinv2-tiny-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e9b51e815bdfbc84df53a609558d0b2e12eddf7a8957dbf269cfcc98ce263ef
- Size of remote file:
- 113 MB
- SHA256:
- 16bed9d885915518c7db54d549d7d608e191469e331621bf6bdcee2be79408f6
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