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:
- ebfdf78a70cb138ddb8c85b5b0add54d70b0f8117f10fb92484a2ed27943fb76
- Size of remote file:
- 4.09 kB
- SHA256:
- 862999a0d7794a59416d95af7f3f0f0e4b2366642b5b17a5863a12a06194fc7b
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