Instructions to use microsoft/resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-50") 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-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/resnet-50: direct link, hf CLI and curl.
- Browser
- Download file 103 MB
-
https://huggingface.co/microsoft/resnet-50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/resnet-50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/resnet-50/resolve/main/pytorch_model.bin
103 MB
- Xet hash:
- 97a1ee491c6fe78e9f4da1cde81576a433b44d14b08bc358e2afc2f5bec6d13b
- Size of remote file:
- 103 MB
- SHA256:
- ff8163a1323333126706d649ce73ecd76e45d241b42d623dea6c723690cafe07
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