Instructions to use sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW") 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("sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW") model = AutoModelForImageClassification.from_pretrained("sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c43a929f93e23e52178a9c5e5dbadec775b22ef64560dcd08acb21e1340f6e6
- Size of remote file:
- 4.66 kB
- SHA256:
- 95a8264f770a3311bc516987ebe731491ac418b9eb46e6f477c2fd8e618fb667
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