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
nielsr HF Staff
Flax implementation https://github.com/huggingface/transformers/pull/21472 @sanchit-gandhi (#3)
4067a27 - Xet hash:
- f03c53f73f734d156fb44c4939de4434895544bc125206d6473ee7b08dc6009b
- Size of remote file:
- 102 MB
- SHA256:
- 1dba0174a596673117b14b7b97d27bbfef1928a94377b18b4cb849b9c8569b90
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