Instructions to use tensorgirl/TFaugvit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorgirl/TFaugvit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tensorgirl/TFaugvit", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import TFAutoModelForImageClassification model = TFAutoModelForImageClassification.from_pretrained("tensorgirl/TFaugvit", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
37c96c3
1
Parent(s): ac9d0d5
Update augvit_config.py
Browse files- augvit_config.py +1 -1
augvit_config.py
CHANGED
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@@ -11,7 +11,7 @@ class AugViTConfig(PretrainedConfig):
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patch_size: int = 32,
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num_classes: int = 1000,
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dim: int = 128,
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depth: int =
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heads: int = 16,
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mlp_dim: int = 256,
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dropout: int = 0.1,
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patch_size: int = 32,
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num_classes: int = 1000,
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dim: int = 128,
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depth: int = 2,
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heads: int = 16,
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mlp_dim: int = 256,
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dropout: int = 0.1,
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