Instructions to use ProbeX/Model-J__SupViT__model_idx_0165 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_0165 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_0165") 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_0165") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0165", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5123e7c84cb71bbd82aece3236046b8255f5b3f2d03961d738347dbc25d9ad26
- Size of remote file:
- 5.37 kB
- SHA256:
- db59014bf676ca3b9f9eb3624e3399a86661d623ba000aa4619aafad8e591ad8
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