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
- Xet hash:
- 2d71c2067232376e07116b15f9d209f534b402af039ad8cd4ec7eeb5695430fc
- Size of remote file:
- 46.9 MB
- SHA256:
- 64320beb5ab72e9819844eb576a38ed9ffbd6edd73dc752a9c920801fb025c96
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