Instructions to use frgfm/repvgg_a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/repvgg_a1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/repvgg_a1") 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/repvgg_a1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8faa2544b5ded7765a2bfe5625f0a854bbf3f9cdec275aa4b90dbc4477c4cee4
- Size of remote file:
- 108 MB
- SHA256:
- c9c8e9d4fd03eb783f4a6501b4b9192b3ad057de0b14402ae27b7d4cbd471d1f
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