Instructions to use ProbeX/Model-J__SupViT__model_idx_0286 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_0286 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_0286") 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_0286") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0286", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04e72f58203562f82e7abf5d0177e9705ecf3c40ea09e8dc0c5c64b8158af8d5
- Size of remote file:
- 5.37 kB
- SHA256:
- c636c136cf73c8200ecda3b1b96ca83441889c945b020876a2d7053bf4ae664f
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