Instructions to use ProbeX/Model-J__SupViT__model_idx_0829 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_0829 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_0829") 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_0829") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0829", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e0a9e637e9215a8dec6d99f55ef4ec4d4d71ae893aeda4854450f0f575ec7b2
- Size of remote file:
- 5.37 kB
- SHA256:
- c41ef6c41b1eb6d2a3334d1660900edc7248db20b8d04bedc844f5db5c536f85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.