Instructions to use frgfm/resnet34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/resnet34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/resnet34") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("frgfm/resnet34", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d32002471a181a1cbd969b1d9eecfbf31291ed7edd9a69f478e44dcafecaf83
- Size of remote file:
- 85.3 MB
- SHA256:
- d9e5dd80ded70d4a0370610047a0e8a2fd041c10c8c17b64d8bfde8525333bc4
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