Instructions to use leftthomas/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leftthomas/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="leftthomas/resnet50", trust_remote_code=True) 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("leftthomas/resnet50", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("leftthomas/resnet50", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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Parent(s): af4062b
Update config.json
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config.json
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2 "architecture": "
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3 "classifier": "fc",
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4 "crop_pct": 0.875,
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5 "first_conv": "conv1.0",
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2 "architecture": "resnet5d",
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3 "classifier": "fc",
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4 "crop_pct": 0.875,
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5 "first_conv": "conv1.0",
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