Instructions to use microsoft/focalnet-tiny-lrf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/focalnet-tiny-lrf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/focalnet-tiny-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("microsoft/focalnet-tiny-lrf") model = AutoModelForImageClassification.from_pretrained("microsoft/focalnet-tiny-lrf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add hidden_sizes config attribute (#1)
- Add hidden_sizes config attribute (94eca8289c1fdb784c4e50488e1b7b8c88de3737) Co-authored-by: Alara Dirik <adirik@users.noreply.huggingface.co>593523b
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Create README.md
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Upload processor
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Upload FocalNetForImageClassification
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initial commit
Alara Dirikf97a488