Instructions to use ProbeX/Model-J__SupViT__model_idx_0795 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_0795 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_0795") 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_0795") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0795") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01e386c418fc8a43aaf587d81b8f736bac54ba7a75bed8e5a30faf8ae37ae2fb
- Size of remote file:
- 5.37 kB
- SHA256:
- 587d25a8e5978699ac4f2dff947161fffe155c24c135be46bdb5df3ba56f5b7a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.