Instructions to use microsoft/swin-large-patch4-window12-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-large-patch4-window12-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-large-patch4-window12-384") 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-large-patch4-window12-384") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-large-patch4-window12-384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/swin-large-patch4-window12-384: direct link, hf CLI and curl.
- Browser
- Download file 791 MB
-
https://huggingface.co/microsoft/swin-large-patch4-window12-384/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/swin-large-patch4-window12-384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/swin-large-patch4-window12-384/resolve/main/pytorch_model.bin
791 MB
- Xet hash:
- 388c13c11a2a6e4062d0d04189ef9d9ca50db0685e202f25e9684920176a6428
- Size of remote file:
- 791 MB
- SHA256:
- e32d94e7786069111670bc4160f74338e1f7baa3759ad0ac7bd5f6f920189e40
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