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 preprocessor_config.json
Browse files- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
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@@ -2,7 +2,7 @@
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"crop_pct": 0.875,
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"do_normalize": true,
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"do_resize": true,
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"
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"image_mean": [
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0.485,
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0.456,
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"crop_pct": 0.875,
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"do_normalize": true,
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"do_resize": true,
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"image_processor_type": "ConvNextImageProcessor",
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"image_mean": [
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0.485,
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0.456,
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