Instructions to use amd/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amd/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amd/resnet50") 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("amd/resnet50") model = AutoModelForImageClassification.from_pretrained("amd/resnet50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1d2b0e33f9634410aa8848824978d2ee04209b79dc5a9e2a5db23156aab5c0b
- Size of remote file:
- 102 MB
- SHA256:
- 57310b7fe473ba3a8fd9d4bcfc7172452bd211557c1e13ea16e8c257bf84696a
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