Instructions to use microsoft/swin-tiny-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-tiny-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-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/swin-tiny-patch4-window7-224: direct link, hf CLI and curl.
- Browser
- Download file 113 MB
-
https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/swin-tiny-patch4-window7-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/pytorch_model.bin
113 MB
- Xet hash:
- 28052effbc820eb5b4f20028d8e5af54ab4b461f430c7c37c0d92bfca2ef4814
- Size of remote file:
- 113 MB
- SHA256:
- f2a3c1bc4ebd5f87ab22331b134c593dd24288c2dbaf7071ad49b4d9c59842d6
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