Image Classification
Transformers
Safetensors
English
siglip
Content Safety
Mature
Content
Detection
Enticing
Sensual
Neutral
Anime
ViT
multi-label
digital spaces
SigLIP2
vision-language encoder
single-label
2e-4
Instructions to use prithivMLmods/Mature-Content-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Mature-Content-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Mature-Content-Detection") 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/Mature-Content-Detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Mature-Content-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# **Intended Use:**
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The **Mature-Content-Detection** model is designed to classify visual content for moderation and filtering purposes. Potential use cases include:
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# **Sample Inference**
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# **Intended Use:**
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The **Mature-Content-Detection** model is designed to classify visual content for moderation and filtering purposes. Potential use cases include:
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