Instructions to use prithivMLmods/Multilabel-Portrait-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Multilabel-Portrait-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Multilabel-Portrait-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Multilabel-Portrait-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Multilabel-Portrait-SigLIP2") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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```py
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Classification Report:
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weighted avg 0.9972 0.9972 0.9972 17776
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```
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license: apache-2.0
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datasets:
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- prithivMLmods/Multilabel-Portrait-18K
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language:
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- en
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base_model:
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- google/siglip2-base-patch16-224
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pipeline_tag: image-classification
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library_name: transformers
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```py
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Classification Report:
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weighted avg 0.9972 0.9972 0.9972 17776
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```
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