Instructions to use frgfm/repvgg_a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/repvgg_a2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/repvgg_a2") 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_a2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fba2494cb1bf823c369d66d25f222e21f57101d7997e269c13f709323ef25f41
- Size of remote file:
- 195 MB
- SHA256:
- 9d4db401d26e971608b74501d25719d5a23c615d3fc1583ce5248efe56d0514c
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