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
Flax implementation https://github.com/huggingface/transformers/pull/21472 @sanchit-gandhi
#3
by Shubhamai - opened
- flax_model.msgpack +3 -0
flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:1dba0174a596673117b14b7b97d27bbfef1928a94377b18b4cb849b9c8569b90
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size 102450913
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