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
Download pytorch_model.bin from microsoft/resnet-18: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/microsoft/resnet-18/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/resnet-18/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/resnet-18/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- ccb8bfdf950105c716c125ee0dfeb294595e9c248922dfcb46cceca76ab0c9d5
- Size of remote file:
- 46.8 MB
- SHA256:
- 420fc189b95a1d31fa95e1cf494c25e3cead05f88480b5578c5efcebeb0db57a
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