Instructions to use DataScienceProject/Vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScienceProject/Vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DataScienceProject/Vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScienceProject/Vit", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update vit_model_test.py
Browse files- vit_model_test.py +1 -1
vit_model_test.py
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@@ -11,7 +11,7 @@ from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, f
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.metrics import recall_score
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from
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.metrics import recall_score
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from vit_model_training import labeling,CustomDataset
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