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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee279e9c83c9dba2913fdb456c8d622b0eb9c724a965d26875097a8b94cd05fe
- Size of remote file:
- 343 MB
- SHA256:
- 7a3419903f7e98968735f744109c4cdc90b7e1bb14bd294d59b4bb4c1d1d17c2
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