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")# pip install -U transformers accelerate # 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
Commit History
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Adding `safetensors` variant of this model
1cc4b4d
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Flax implementation https://github.com/huggingface/transformers/pull/21472 @sanchit-gandhi (#3)
- Flax implementation https://github.com/huggingface/transformers/pull/21472 @sanchit-gandhi (733d69024f18eeb29197661800af678d3ec6b62a) Co-authored-by: Shubhamai <Shubhamai@users.noreply.huggingface.co>4067a27
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Update README.md
e8f8d20
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Add TF weights (#2)
- Add TF weights (d84150477f71747a53a4a22fd99fd70763e12295)f5104f6
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Update README.md
5fdf63a
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Create README.md (#1)
- Create README.md (5fec255894d497aac3cd6e299432dab85444a9f5) - Remove imagenet tag (4ef075409f32da36ade0c89970fbde12605e6acf) Co-authored-by: Régis Pierrard <regisss@users.noreply.huggingface.co>aec901e
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Add feature extractor
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Add model
03cc283 -
initial commit
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