Instructions to use Abdullah6030/vitmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abdullah6030/vitmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Abdullah6030/vitmodel") 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("Abdullah6030/vitmodel") model = AutoModelForImageClassification.from_pretrained("Abdullah6030/vitmodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 511b2925c7234e98d1e01f2eb570b039c36d9593f2f05cbbcbfa55858a6492a5
- Size of remote file:
- 5.05 kB
- SHA256:
- 3cd828a165efa08c0a8def3da09c8f7e1bd1584f8048215b140146c695469f07
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