Instructions to use prithivMLmods/Deepfake-QualityAssess-88M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Deepfake-QualityAssess-88M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Deepfake-QualityAssess-88M") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("prithivMLmods/Deepfake-QualityAssess-88M") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Deepfake-QualityAssess-88M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- Deepfake
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# **Deepfake-QualityAssess-88M**
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Deepfake-QualityAssess-88M is an image classification model for quality assessment of good and bad quality deepfakes. It is based on Google's ViT model (`google/vit-base-patch32-224-in21k`).
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tags:
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- Deepfake
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# **Deepfake-QualityAssess-88M**
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Deepfake-QualityAssess-88M is an image classification model for quality assessment of good and bad quality deepfakes. It is based on Google's ViT model (`google/vit-base-patch32-224-in21k`).
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