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:
- 3e36320e6d081d9850a77801c915794f15cdee81a991d49f3709b83c185e89f7
- Size of remote file:
- 175 MB
- SHA256:
- 6555aa443e3026a3ef027d51e2499bedba1b3bb07d3defdbddae9d6028f683e9
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