Instructions to use pytholic/vit_classification_huggingface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pytholic/vit_classification_huggingface with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pytholic/vit_classification_huggingface") 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("pytholic/vit_classification_huggingface") model = AutoModelForImageClassification.from_pretrained("pytholic/vit_classification_huggingface", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 148f81c8fa6075c8e0c2139ce4eab42e002af54540f7cf97260c48d675d95ce7
- Size of remote file:
- 18.6 kB
- SHA256:
- af05ff6590a644998fed5933eb64ca7fd06b235eb6cdd89f8acb587dbdafbbb6
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