Instructions to use microsoft/swinv2-tiny-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-tiny-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-tiny-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-tiny-patch4-window8-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c1a87e4f1a6f48b1be8ea6e70d75a1b5abf39e598efa7360a00cbb2df04e19b
- Size of remote file:
- 113 MB
- SHA256:
- dabb5022c11b5ddc5e5c8e456000563d4e7a1bd59b06cd1a9bb03aa0d5650d58
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