Instructions to use microsoft/swinv2-base-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-base-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-base-patch4-window8-256") 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-window8-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window8-256") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c8dc5cd75df9f776c46abfb6b97eec44bf6bca6ba2acef68b7e3488e926c416
- Size of remote file:
- 352 MB
- SHA256:
- b7aeded1e50fc6e9dca604336e537c429d5d3832947d630a2edef7b39922de9e
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