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
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config.json
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{
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"ResNetForImageClassification"
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"depths": [
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"_commit_hash": 596fb6e892215ff57e75bb5385439ef0fe268077,
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"architectures": [
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"ResNetForImageClassification"
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"depths": [
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