Instructions to use Prot10/swin-tiny-patch4-window7-224-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/swin-tiny-patch4-window7-224-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/swin-tiny-patch4-window7-224-for-pre_evaluation") 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("Prot10/swin-tiny-patch4-window7-224-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/swin-tiny-patch4-window7-224-for-pre_evaluation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47330fd17b633773937737469cea9ade9ccf75a760f733310176d6ec43016c80
- Size of remote file:
- 110 MB
- SHA256:
- 41f89a5638105f81faa2b6e6df6e56e36d75314d67d6a4d63b4c94336595af82
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