Instructions to use nateraw/test_model_a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/test_model_a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/test_model_a") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/test_model_a") model = AutoModelForImageClassification.from_pretrained("nateraw/test_model_a") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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name: image_folder
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type: image_folder
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args: default
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---
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name: image_folder
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type: image_folder
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args: default
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base_model: lysandre/tiny-vit-random
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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