Instructions to use frgfm/cspdarknet53 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/cspdarknet53 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/cspdarknet53") 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/cspdarknet53", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94d8bfac6c1e81fb37264422f5503a31d311a94b812a395a3615523ba8eb259a
- Size of remote file:
- 107 MB
- SHA256:
- 9803cd6c7dec03f1b48d9a99afbcb6785e0d76561f9db5376d7bbff1f100ebb6
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