Instructions to use ProbeX/Model-J__SupViT__model_idx_0584 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_0584 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_0584") 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_0584") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0584", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cecaef8cc6913a30da5cabea576f57a4b505e18836bcbdec31ecbe5dce5dee13
- Size of remote file:
- 5.37 kB
- SHA256:
- b975d5beafe9f1b1aaa52e3e5b62e2f9b99e5c2705fecc9b107dba7411df958b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.