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, device_map="auto") 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- imagenet-1k
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metrics:
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- accuracy
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pipeline_tag: image-classification
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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example_title: Tiger
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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example_title: Teapot
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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example_title: Palace
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# TFaugvit
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- imagenet-1k
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metrics:
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- accuracy
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# TFaugvit
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