Instructions to use prithivMLmods/SigLIP2-ImageShield-90M-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SigLIP2-ImageShield-90M-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/SigLIP2-ImageShield-90M-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/SigLIP2-ImageShield-90M-224") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/SigLIP2-ImageShield-90M-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# **SigLIP2-ImageShield-90M**
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> **SigLIP2-ImageShield-90M** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class image classification**. Built on the **SiglipForImageClassification** architecture, the model is designed to identify and categorize visual content for explicit, suggestive, and safe media filtering.
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> [!note]
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> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
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import torch
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# Load model and processor
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model_name = "prithivMLmods/SigLIP2-ImageShield-90M" # Replace with your model path if needed
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model = SiglipForImageClassification.from_pretrained(model_name)
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processor = AutoImageProcessor.from_pretrained(model_name)
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# **SigLIP2-ImageShield-90M-224**
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> **SigLIP2-ImageShield-90M-224** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class image classification**. Built on the **SiglipForImageClassification** architecture, the model is designed to identify and categorize visual content for explicit, suggestive, and safe media filtering.
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> [!note]
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> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
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import torch
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# Load model and processor
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model_name = "prithivMLmods/SigLIP2-ImageShield-90M-224" # Replace with your model path if needed
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model = SiglipForImageClassification.from_pretrained(model_name)
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processor = AutoImageProcessor.from_pretrained(model_name)
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