Instructions to use ProbeX/Model-J__SupViT__model_idx_0060 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_0060 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_0060") 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_0060") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0060") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6baca5ae2e789d6cd88aa3c426ed2922957ddc6279e38bdae78467bc78b1cdb
- Size of remote file:
- 5.37 kB
- SHA256:
- 41bbcdc5630d9bdbe7ee12bf2082f95244f39d4dab05a84608523ab240f29535
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.