Instructions to use ProbeX/Model-J__SupViT__model_idx_0755 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_0755 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_0755") 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_0755") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0755", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c485ae5c8a2e8b1910a538ec385c9269d4b5f2b75042ebfb53c3a668a220c32d
- Size of remote file:
- 5.37 kB
- SHA256:
- 75b73198997d63440ffd4c579844edaee649dcd17b2bfc12ed77a1eef915016d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.