Instructions to use Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/swinv2-base-patch4-window8-256-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/swinv2-base-patch4-window8-256-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b7bfb6e337c1a514b830a3aae31bee967d271da0bad4dc6eda043ddb3d35c53
- Size of remote file:
- 4.09 kB
- SHA256:
- 171f0eca1711ca0252518fa92c19180c90bdb7ae3e8742059618936a6233114b
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