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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/resnet-18") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (82f42eb8d95bdc3ca8d2b098c499b8817a3bca97) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>f45a6fa
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Add TF weights (#1)
- Add TF weights (ea4191cd0ee345d27d72bf01001bdbddc41926a2)2f536bd
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Fix code example
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Add feature extractor
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initial commit
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