Instructions to use ShihTing/PanJuOffset_TwoClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShihTing/PanJuOffset_TwoClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ShihTing/PanJuOffset_TwoClass") 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("ShihTing/PanJuOffset_TwoClass") model = AutoModelForImageClassification.from_pretrained("ShihTing/PanJuOffset_TwoClass") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebe35e7c315925bf45e2e94f12ac6c1996515866c829393309565a402e8b6c5b
- Size of remote file:
- 343 MB
- SHA256:
- e1be63399818c90071a351a1b8132e9c9a8ac28f199a8a3b770e5567558ee4bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.