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
Update config.json
Browse files- config.json +0 -2
config.json
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"layer_type": "bottleneck",
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"model_type": "resnet",
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"num_channels": 3,
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"torch_dtype": "float32",
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"transformers_version": "4.18.0.dev0"
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}
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"layer_type": "bottleneck",
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"model_type": "resnet",
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"num_channels": 3,
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}
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