Instructions to use frgfm/rexnet2_0x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/rexnet2_0x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/rexnet2_0x") 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/rexnet2_0x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57360168af8574cb859b945356557e08874f20135970d7b702385ba49e8848d7
- Size of remote file:
- 55.3 MB
- SHA256:
- 77b7c4529ad1e3aa94f7b5d337c85269b3e20251f916f3bbe15512b53eb335ed
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