Instructions to use microsoft/resnet-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-18") 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("microsoft/resnet-18") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18") - Inference
- Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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tags:
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- vision
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- image-classification
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datasets:
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- imagenet-1k
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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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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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# ResNet
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license: apache-2.0
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datasets:
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- imagenet-1k
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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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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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# ResNet
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