Instructions to use dima806/deepfake_vs_real_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/deepfake_vs_real_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection") 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("dima806/deepfake_vs_real_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/deepfake_vs_real_image_detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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Checks whether an image is real or fake (AI-generated).
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**Note to users who want to use this model in production**
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Beware that this model is trained on a dataset collected about 3 years ago.
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Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift.
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To mitigate that, I urge you to retrain the model using the latest available labeled data.
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Checks whether an image is real or fake (AI-generated).
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**Note to users who want to use this model in production**
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| 13 |
Beware that this model is trained on a dataset collected about 3 years ago.
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| 14 |
Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift.
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To mitigate that, I urge you to retrain the model using the latest available labeled data.
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