Instructions to use Francesco/resnet152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet152 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet152", device_map="auto") 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") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet152", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3ee36b45a806257eacfa51ee69cf2723c3f8771a91803bcc2f1929365e366a7
- Size of remote file:
- 242 MB
- SHA256:
- 691eb1ad70d7d62db869c012b76536ca39f0a0e5cbe2be7cabde4c22ab4efa7f
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