Instructions to use ProbeX/Model-J__SupViT__model_idx_0546 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_0546 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_0546") 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_0546") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0546") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d3d7418a4c091d1d1258b0df7c94325ae0ea078fad422ab8c2165fcf3321a9f
- Size of remote file:
- 5.37 kB
- SHA256:
- cb9cf129ada69836244daba76653fd252a6b2b73a51f31045adae5a00eed5359
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.