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
Download images/cane.jpeg from pytholic/vit_classification_huggingface: direct link, hf CLI and curl.
- Browser
- Download file 9.45 kB
-
https://huggingface.co/pytholic/vit_classification_huggingface/resolve/main/images/cane.jpeg
- Command line
-
hf download hf://pytholic/vit_classification_huggingface/images/cane.jpeg
-
curl -L -o cane.jpeg https://huggingface.co/pytholic/vit_classification_huggingface/resolve/main/images/cane.jpeg
9.45 kB

- Xet hash:
- 9a9fc576f161f534f47d89f8f001f0d4de576b5b619a82ddd4b8b08c52d078df
- Size of remote file:
- 9.45 kB
- SHA256:
- 9e5e1c98ef94813b4355463fee440908fc17772f2e509928d4797c67957a6919
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