Instructions to use ProbeX/Model-J__SupViT__model_idx_0163 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_0163 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_0163") 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_0163") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0163", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a8a7169b16d93654b935cfa4173b873b6a2a29faf9ecea3dcd970665497325
- Size of remote file:
- 5.37 kB
- SHA256:
- cf7c7fff62fe5362d189742524d5c941148b5a2b3a6f5830bc31a602b8c20c0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.