Instructions to use ProbeX/Model-J__SupViT__model_idx_0656 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_0656 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_0656") 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_0656") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0656", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e72fcab03f3c1fb9e977791bd82ba35307231f8e955ff647f02652e9eea23cc1
- Size of remote file:
- 5.37 kB
- SHA256:
- c1a7c6793b1dc933cd4aadb3e87f84239c89ea0fb4c42592c5ea1ce07ca3b2d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.