Instructions to use ProbeX/Model-J__SupViT__model_idx_0376 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_0376 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_0376") 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_0376") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0376", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a914af1dd73bbf42b73facb291b229eb56b7d6a6175ecc699885b9c337d17285
- Size of remote file:
- 5.37 kB
- SHA256:
- 93395a6e4e2562c55c311aa353f6eee2032b2dbe2366f54f4f4c88a8c1142dc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.