Instructions to use ProbeX/Model-J__SupViT__model_idx_0708 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_0708 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_0708") 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_0708") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0708") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3c954d3b98879be5bf05f9de05336ff7d73ecc74b375ff328469b4e76983042
- Size of remote file:
- 343 MB
- SHA256:
- 4856dd336082aa9689fa42fb3cff8854be4e30916f1e69a2959fe3bec3378166
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.