Instructions to use ProbeX/Model-J__SupViT__model_idx_0747 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_0747 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_0747") 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_0747") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0747", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 060b57e974a7fe44fc8f3f87494597c74e91670aff60bffbc7ba57f1cdc0eebd
- Size of remote file:
- 5.37 kB
- SHA256:
- 94e31dc590793b9692575d09555946abf0ea9ed8c3f7c74cc1f827ed12e433db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.