Instructions to use ProbeX/Model-J__SupViT__model_idx_0123 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_0123 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_0123") 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_0123") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93e092e96b7d17dd8b98f15edcd4910801abf6235dab39ea57c3027f67c6968a
- Size of remote file:
- 5.37 kB
- SHA256:
- c49fcaf0e28aa58f31f5fe0c4f0ff19b362051bc1be8607b44277334eb6c9ce8
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