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:
- a82b1cfb83afe9e550d1e6463f353daca9b26835e6a08c9eebf86bc616dc1ce2
- Size of remote file:
- 85.1 MB
- SHA256:
- 5f52a57096dd25d012064abaa54ce8bfea84b9b97da90e160c2f5914d03b71ee
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