Instructions to use ProbeX/Model-J__SupViT__model_idx_0995 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_0995 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_0995") 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_0995") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0995") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0567007d86110dd05094f44be9fe19ee0d1ebeac7105736047d40311cd96f357
- Size of remote file:
- 5.37 kB
- SHA256:
- 56353a069d62b0a380d2bcc1c7694491348e1c2cb3d3f903ef7aacd8c7cd6c89
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