Instructions to use ENOT-AutoDL/imagenet-benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENOT-AutoDL/imagenet-benchmark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ENOT-AutoDL/imagenet-benchmark") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ENOT-AutoDL/imagenet-benchmark", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: apache-2.0
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datasets:
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- imagenet-1k
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library_name: torchvision
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pipeline_tag: image-classification
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tags:
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- onnx
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license: apache-2.0
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datasets:
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- imagenet-1k
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pipeline_tag: image-classification
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tags:
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- onnx
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