Instructions to use ProbeX/Model-J__SupViT__model_idx_0342 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_0342 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_0342") 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_0342") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0342") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8869ef6e275e0255151e383b1c61f58ca5d060e2bade35c1c982261e9c3db73f
- Size of remote file:
- 5.37 kB
- SHA256:
- 8d15bc618cf1673ba942671e384b80d0349374aa690bd50ef48a61b4a7e87583
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.