Instructions to use microsoft/focalnet-base-lrf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/focalnet-base-lrf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/focalnet-base-lrf") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/focalnet-base-lrf") model = AutoModelForImageClassification.from_pretrained("microsoft/focalnet-base-lrf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 276e4937378e26138816733b47d11ce76e51d31ece98ddaf79ab052543d583f7
- Size of remote file:
- 355 MB
- SHA256:
- 45ce2dac7afb6c349159fbb3149570cb17d4969c51c61ae8a578efadf716f30e
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