Instructions to use Francesco/resnet18-224-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet18-224-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet18-224-1k") 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("Francesco/resnet18-224-1k") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet18-224-1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be8ae3eb462af9ce37bc41d5e8afa3f10e26e8228733a5ceb859d68c9cc839de
- Size of remote file:
- 46.8 MB
- SHA256:
- 3d5dc043c9d0e16ad2be6cfaf776b0e613ef9a9d10f63892d602b4264da75602
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