Instructions to use microsoft/swinv2-small-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-small-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-small-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("microsoft/swinv2-small-patch4-window8-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-small-patch4-window8-256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85f871bc393804527002b54a300e88d75e75a5ccdd5a209e666e2720d3cb46b7
- Size of remote file:
- 199 MB
- SHA256:
- f6c5299bb53089cf164c58b6c695d33ac83cf2989118c19b989e7738e1e7602c
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