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:
- ea9774c11725dc5f3753b70509d0a92764305ecd5fc7f5dbfa548297f52bf3f3
- Size of remote file:
- 27 kB
- SHA256:
- 99912e44e8af7cb20c3236b3de814304654baa125b7b0ca9fc84f5e099be05d5
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