Instructions to use Sathya77/ViT_MvTec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sathya77/ViT_MvTec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sathya77/ViT_MvTec") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sathya77/ViT_MvTec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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metrics:
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- accuracy
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- anomaly score distribution
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- thresholding (mean + std)
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- top-k visualization
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base_model:
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- google/vit-base-patch16-224
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pipeline_tag: image-classification
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library_name: transformers
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---
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