Instructions to use microsoft/swin-small-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-small-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-small-patch4-window7-224") 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-small-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-small-patch4-window7-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5e8df916461d8588069c0d5f29645b01d3374606e5310c0d9a11ef794b176c6
- Size of remote file:
- 199 MB
- SHA256:
- b5a1286d4379f1f2e4f8a46257a74a8507a1d3338ee2c7afb62f10490b1b1370
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