Instructions to use microsoft/swinv2-small-patch4-window16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-small-patch4-window16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-small-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-small-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-small-patch4-window16-256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec3896227a428619e34e5f5f3aa2efd131ac2524c1d302d3c59e38f3e97c05b
- Size of remote file:
- 199 MB
- SHA256:
- fe537a9e86f8ec7dfbb135199d5677b8b0557daeea389061e7abfe8059e013c7
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