Instructions to use ProbeX/Model-J__SupViT__model_idx_0694 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_0694 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_0694") 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_0694") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0694") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 320716879530868ae90dbb311049e331abcd3bff23f53279ba1b4e61412e5af6
- Size of remote file:
- 5.37 kB
- SHA256:
- 05cdf91b6fa992964f93320f80f97143b940636dcc54e6fd7051e1d9f285acb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.