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:
- 1565b841f8f9476d23dcaa50be951c9868d78db33b52ac1eb3e903ede24a19bd
- Size of remote file:
- 113 MB
- SHA256:
- daf9001f464d1e7f5a31abdd3478a13c7fb012fc8795cab2c3fbb341151ccac7
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