Instructions to use PREMAADC/vit-base-ham10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PREMAADC/vit-base-ham10000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="PREMAADC/vit-base-ham10000") 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("PREMAADC/vit-base-ham10000") model = AutoModelForImageClassification.from_pretrained("PREMAADC/vit-base-ham10000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e7bc9e65fb25cce8ea66f618561e5d8da05634c85b1a4839bdd9af33ba01439
- Size of remote file:
- 5.2 kB
- SHA256:
- 657d35433829ba7c8021796c0cdc1cadb0e8bed5b1c60ca4323a563e55c3c841
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