Instructions to use nielsr/focalnet-small-lrf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/focalnet-small-lrf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nielsr/focalnet-small-lrf") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nielsr/focalnet-small-lrf") model = AutoModelForImageClassification.from_pretrained("nielsr/focalnet-small-lrf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nielsr/focalnet-small-lrf: direct link, hf CLI and curl.
- Browser
- Download file 202 MB
-
https://huggingface.co/nielsr/focalnet-small-lrf/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nielsr/focalnet-small-lrf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nielsr/focalnet-small-lrf/resolve/main/pytorch_model.bin
202 MB
- Xet hash:
- 6d52ed500f0eb1aef298642230fbbe120b592d6e83b00e3c62680e193749c91c
- Size of remote file:
- 202 MB
- SHA256:
- 31cfa22aaa28522461b279cd3714cf7c304646f70e81afc51451ba97cb1f5a4e
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