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") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
f5104f6
1
Parent(s): 5fdf63a
Add TF weights (#2)
Browse files- Add TF weights (d84150477f71747a53a4a22fd99fd70763e12295)
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:57afa67ca9f9aa1657f38f100f8e5c7fc49550dafa7226793b48dae4426f5a69
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size 102753944
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