Instructions to use ProbeX/Model-J__SupViT__model_idx_0189 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_0189 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_0189") 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_0189") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0189") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3e496fc3efaf0ad47dced89878347ef9807555e384f2293f725c2a2eb08786a
- Size of remote file:
- 5.37 kB
- SHA256:
- 5ebeb578329d494303fb4e66404e886cea1b5c9ab1558fccebef7e5b0926cf33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.