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
- Xet hash:
- 61cc5d34869b40003a93b0327f1ce567b9c4ff28eb13e499e4b5d558582f0279
- Size of remote file:
- 5.3 kB
- SHA256:
- 005fcbfb57c2d649d2aec3ffebfbe2e1b52c7fde480226b895052e7bdab99725
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