Instructions to use microsoft/focalnet-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/focalnet-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/focalnet-tiny") 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-tiny") model = AutoModelForImageClassification.from_pretrained("microsoft/focalnet-tiny", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/focalnet-tiny: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/microsoft/focalnet-tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/focalnet-tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/focalnet-tiny/resolve/main/pytorch_model.bin
114 MB
- Xet hash:
- cb7ac7c85acac092ecf5842559a380b22372a7241a38352ef6223a807de00d93
- Size of remote file:
- 114 MB
- SHA256:
- e0581238c22e5861094b7878e5127d2598da9918de28266994a8869b5554b9a2
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