Instructions to use ProbeX/Model-J__SupViT__model_idx_0370 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_0370 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_0370") 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_0370") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0370", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bdc9ef67f9cfba81762495dd6f320b40be329fa0d0a0fc6abbb3132fb66b841
- Size of remote file:
- 5.37 kB
- SHA256:
- 6ec18f57203e8e4efda357c9d72013aad280bf491061abb4e2c6e03bbae0d16a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.