Instructions to use ProbeX/Model-J__SupViT__model_idx_0572 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_0572 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_0572") 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_0572") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0572", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dc24e36ea2bdf7f6b862933faaeb636e0885e83a62bab86d5e3cc20e0ec1790
- Size of remote file:
- 5.37 kB
- SHA256:
- 99d78ee96d56cbface455adb0336f9c94771732dcf3a892c24e031e1cd76d2ea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.