Instructions to use Francesco/resnet101-224-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet101-224-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet101-224-1k") 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/resnet101-224-1k") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet101-224-1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62741703a73549f11de1b2e43bfe178defa53275ce441393cb6601a08cc1b77a
- Size of remote file:
- 179 MB
- SHA256:
- e3fdd78d412cdaebdbde215c7b2be90c84e2d07885651be1a8164855724bd2d5
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