Instructions to use Francesco/resnet152-224-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet152-224-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet152-224-1k") 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/resnet152-224-1k") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet152-224-1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b66f67ae5703d0919a885543531e461a4b86d1f2e59285b3a773fdb7ef572df0
- Size of remote file:
- 242 MB
- SHA256:
- 480a881ce1eb591cf9b40dbe770233fc1b2bb0fa0c8e8d2069a3becee21a5d23
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