Instructions to use microsoft/swinv2-base-patch4-window16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-base-patch4-window16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-base-patch4-window16-256") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-base-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/swinv2-base-patch4-window16-256: direct link, hf CLI and curl.
- Browser
- Download file 352 MB
-
https://huggingface.co/microsoft/swinv2-base-patch4-window16-256/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/swinv2-base-patch4-window16-256/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/swinv2-base-patch4-window16-256/resolve/main/pytorch_model.bin
352 MB
- Xet hash:
- 362f7dde1e2f6bae9414680dcd8d4bbbddd4e67840e258374814e987bb8b01c2
- Size of remote file:
- 352 MB
- SHA256:
- c9307c9aa168a730c370d472783ae8274408a059e95245e0d7fcf1a1d91cf9aa
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