Instructions to use autoevaluate/image-multi-class-classification-not-evaluated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/image-multi-class-classification-not-evaluated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="autoevaluate/image-multi-class-classification-not-evaluated") 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("autoevaluate/image-multi-class-classification-not-evaluated") model = AutoModelForImageClassification.from_pretrained("autoevaluate/image-multi-class-classification-not-evaluated") - Notebooks
- Google Colab
- Kaggle
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- autoevaluate/mnist-sample
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metrics:
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- accuracy
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model-index:
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- name: image-classification
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: mnist
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type: mnist
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args: mnist
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9833333333333333
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duplicated_from: autoevaluate/image-multi-class-classification
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- autoevaluate/mnist-sample
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metrics:
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- accuracy
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duplicated_from: autoevaluate/image-multi-class-classification
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