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
Commit ·
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Parent(s): b7b70bc
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config.json
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{
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"architectures": [
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"ResnetModel"
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],
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"block_type": "bottleneck",
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"layers": [
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"num_classes": 1000,
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"torch_dtype": "float32",
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"transformers_version": "4.14.1"
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}
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