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:
- 7449258c8bb7cacb6d0ba7e1f3e14fa3fbdbdd9246c2c20784f85c95141ec58f
- Size of remote file:
- 348 MB
- SHA256:
- 5bce62e07fcf1e23ab30a0268800e51b82035e0d84263052b9673a68e3335fbd
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