Instructions to use microsoft/focalnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/focalnet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/focalnet-base") 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("microsoft/focalnet-base") model = AutoModelForImageClassification.from_pretrained("microsoft/focalnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/focalnet-base: direct link, hf CLI and curl.
- Browser
- Download file 353 MB
-
https://huggingface.co/microsoft/focalnet-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/focalnet-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/focalnet-base/resolve/main/pytorch_model.bin
353 MB
- Xet hash:
- 38a445f57b313f6d3eafe784af4de00219fb51eef0d76f9261c639fc8f13011c
- Size of remote file:
- 353 MB
- SHA256:
- b202c1a8c77a3d03b6a2e260d1c9d8db5cc0339ffacb047371e5876dda87fae8
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