Instructions to use ProbeX/Model-J__SupViT__model_idx_0950 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_0950 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_0950") 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_0950") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0950") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d058c84cb5fb2619f1f019e870ee24d28431e34b403a9dbaf28421c464ce545
- Size of remote file:
- 5.37 kB
- SHA256:
- f4de1ee16295d7d170aca800e00e1d51b3cd72beb652e03d6838212efcc1ca9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.