Instructions to use ProbeX/Model-J__SupViT__model_idx_0295 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0295 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0295") 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("ProbeX/Model-J__SupViT__model_idx_0295") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0295") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be79bbf593dd371313ae35dd616a058eb88eabbecb12e5a336a8874d25f11c98
- Size of remote file:
- 343 MB
- SHA256:
- 01422b4fa7dd6d7e2a1294b9c263e640e73ffc63202f7ab04f8b18e766c6a76d
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