Instructions to use microsoft/swin-base-patch4-window12-384-in22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-base-patch4-window12-384-in22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-base-patch4-window12-384-in22k") 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/swin-base-patch4-window12-384-in22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-base-patch4-window12-384-in22k", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/swin-base-patch4-window12-384-in22k: direct link, hf CLI and curl.
- Browser
- Download file 441 MB
-
https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/swin-base-patch4-window12-384-in22k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k/resolve/main/pytorch_model.bin
441 MB
- Xet hash:
- 7372d70f9d7a9553f8e974030d0bd56f340119287117511ec3c96244dfb99103
- Size of remote file:
- 441 MB
- SHA256:
- f556511d21fe34fcffceaa3168136a3bf79c83c99894da193f6dc843a5c3e985
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