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
Update README.md
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README.md
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# PanJu offset detect by image
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Use fintune from google/vit-base-patch16-224(https://huggingface.co/google/vit-base-patch16-224)
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---
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license: apache-2.0
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tags:
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- vision
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- image-classification
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widget:
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- src: https://datasets-server.huggingface.co/assets/ShihTing/IsCausewayOffset/--/ShihTing--IsCausewayOffset/validation/0/image/image.jpg
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example_title: Ex1
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---
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# PanJu offset detect by image
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Use fintune from google/vit-base-patch16-224(https://huggingface.co/google/vit-base-patch16-224)
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