Instructions to use ProbeX/Model-J__SupViT__model_idx_0760 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_0760 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_0760") 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_0760") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0760") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79a285b619c885e710ca3867ee58c0241c2909b81b5852b84f71ee4e4d48ab4d
- Size of remote file:
- 5.37 kB
- SHA256:
- 01fcc70450e6419d835b5cc298313bf5524affa2550072cc932c141f2c7e7173
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.