Instructions to use microsoft/swin-large-patch4-window7-224-in22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-large-patch4-window7-224-in22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-large-patch4-window7-224-in22k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/swin-large-patch4-window7-224-in22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-large-patch4-window7-224-in22k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1f28779b0fdc5996bc872791df2428535dd4e6f656a778995638cb1fd1e79f5
- Size of remote file:
- 915 MB
- SHA256:
- da0125ee28d8ff8f6bbf99e8a41142a996fccb015d5dc64e38fd6841179d3f2b
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