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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sathya77/ViT_MvTec", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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import joblib, numpy as np, torch
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# Load model + embeddings
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model = ViTModel.from_pretrained("
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knn = joblib.load("knn.pkl")
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train_embeddings = np.load("train_embeddings.npy")
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import joblib, numpy as np, torch
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# Load model + embeddings
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model = ViTModel.from_pretrained("Sathya77/ViT_MvTec")
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knn = joblib.load("knn.pkl")
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train_embeddings = np.load("train_embeddings.npy")
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