Instructions to use ProbeX/Model-J__SupViT__model_idx_0244 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_0244 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_0244") 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_0244") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0244") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b37d81cb91b025c59ca26f3bbac955399f1abc891418f9705fc16c7fc70fdb26
- Size of remote file:
- 5.37 kB
- SHA256:
- 47de733f5fcfe5d3f97e516c46d6a4a831ad7c4cbebc00668fbf0526baabf9ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.