Instructions to use microsoft/swin-base-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-base-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-base-patch4-window7-224") 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-window7-224") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-base-patch4-window7-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/swin-base-patch4-window7-224: direct link, hf CLI and curl.
- Browser
- Download file 352 MB
-
https://huggingface.co/microsoft/swin-base-patch4-window7-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/swin-base-patch4-window7-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/swin-base-patch4-window7-224/resolve/main/pytorch_model.bin
352 MB
- Xet hash:
- 781959e1eba3c630d91722cd3ab9514ab4e1eed49165382ab17aaf907ddcad56
- Size of remote file:
- 352 MB
- SHA256:
- 4df810a56930047b1e181a5119da088963c608b57fb4e2280700d4d4aaa1ff31
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