Instructions to use prithivMLmods/Deepfake-Quality-Classifier-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Deepfake-Quality-Classifier-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Deepfake-Quality-Classifier-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/Deepfake-Quality-Classifier-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Deepfake-Quality-Classifier-SigLIP2") - Notebooks
- Google Colab
- Kaggle
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# **Deepfake-Quality-Classifier-SigLIP2**
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- **Deepfake Quality Assessment:** Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.
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- **Content Moderation:** Assisting in filtering low-quality deepfake images in digital media platforms.
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- **Forensic Analysis:** Supporting researchers and analysts in assessing the credibility of synthetic images.
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- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.
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- Classifier
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# **Deepfake-Quality-Classifier-SigLIP2**
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- **Deepfake Quality Assessment:** Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.
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- **Content Moderation:** Assisting in filtering low-quality deepfake images in digital media platforms.
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- **Forensic Analysis:** Supporting researchers and analysts in assessing the credibility of synthetic images.
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- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.
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