Instructions to use microsoft/swinv2-base-patch4-window12-192-22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-base-patch4-window12-192-22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-base-patch4-window12-192-22k") 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/swinv2-base-patch4-window12-192-22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3ae5452cf2c8cb7dd4a1e7a6f58e7f9c17e9bc766dfd790bac00aeafa69d54
- Size of remote file:
- 437 MB
- SHA256:
- a928b9553eb27eea3fccb2d5714f77382f4530117ed40627d8411bf08586ac7d
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