Instructions to use ProbeX/Model-J__SupViT__model_idx_0768 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_0768 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_0768") 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_0768") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0768") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1af9667fbc55352d6c41bfc02963a16a4f3016fcc17ff84024b3c479ba4c297
- Size of remote file:
- 5.37 kB
- SHA256:
- bbe7c9993f22538f1c0dbb954b33e048cd33ec46e37cd70a8299db2dec00007c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.